What are vector, lexical, and semantic search—and how do they work?

What is lexical search?

Lexical search is the most traditional form of search. In fact, you’ve likely been using it every day for several years. It works by matching the exact words in your query with indexed text—no interpretation, just literal precision.

How lexical search works

Lexical search scans for keyword matches in titles, file names, or content. For example, if you type “Q4 content calendar,” it will return files that contain those exact terms. But it won’t surface a document called “Marketing Timeline Winter 2024”—even if it’s contextually relevant—unless the phrase appears verbatim.

Lexical search is:

• Great for known-file lookups
• Fast and lightweight
• Limited by surface-level matching
  

It’s highly effective for small datasets, controlled vocabularies, or when you know exactly what you’re looking for. But as your content grows and diversifies, its limitations become more noticeable.

What is semantic search?

Semantic search takes things a step further. Instead of relying solely on keywords, it tries to understand what you actually mean.

How does semantic search work?

By using NLP (Natural Language Processing) and AI models, semantic search recognizes the relationships between words, concepts, and queries. It understands synonyms, phrasing variations, and even loosely defined requests.

For example, a search like “Find that pricing model deck from last month” could return a file titled “Revenue Forecast Q2”—even if the words pricing or deck don’t appear. That’s because semantic search focuses on meaning and usage, not literal matches.

It’s the same technology behind smart assistants like Siri, Google Assistant, or even Google search. Ask “How do I get paint out of jeans?” and these tools will return results like “Removing stains from denim”—even if your exact words aren’t used.

Semantic search is especially useful for:

• Exploratory queries
• Vague or imprecise searches
• Navigating large, unstructured datasets
  

By bridging the gap between user intent and content, semantic search unlocks more natural, intuitive discovery—helping people find what they meant to search for, not just what they typed.

What is vector search?

Vector search is an advanced technique that uses machine learning to represent content—like text, images, or video—as numerical vectors, also known as embeddings. These vectors capture the meaning and context of the data, enabling the system to search by measuring how close or similar the vectors are in a high-dimensional space.

Unlike traditional keyword matching, vector search identifies semantic relationships and contextual similarities, allowing it to deliver more accurate and relevant results—even when the query doesn’t use exact terms found in the data.

It’s okay if that sounds a little like science fiction. While semantic search feels intuitive—mirroring how we, as humans, connect words and interpret meaning—vector search gets to a similar outcome using nothing but numbers.

How does vector search work?

Instead of relying on keywords, vector search looks at meaning. For example, a search for “CEO onboarding checklist” may return docs titled “Executive starter pack” or “Leadership transition plan”—even if they don’t share keywords. Vector search understands the intent behind the query, not just the words used.

Vector search powers features like:

• Image or voice search
• Personalized recommendations
• AI summarization and Q&A
  

By embedding and comparing content at a conceptual level, vector search enables flexible, intuitive information discovery—especially in large sets of unstructured data. It’s a foundational capability for AI search systems, helping them deliver smarter, more relevant results across formats.

Lexical vs. semantic vs. vector: What’s the difference?

The key difference between lexical, semantic, and vector search lies in their approach to understanding and retrieving information.

Lexical search relies on exact keyword matches, semantic search focuses on understanding the meaning and intent behind the query, and vector search uses vector-based representations to capture semantic relationships.

The best applications for each vary:

• Lexical search is suitable for simple text retrieval
• Semantic search excels in intent-based searches
• Vector search is ideal for AI-driven recommendations and multimedia content
  

The most effective business search tools—like Dropbox Dash—don’t rely on a single method. Dash combines elements of lexical, semantic, and vector search to help teams find what they need faster, with context and precision, across all their connected tools.

Benefits of advanced search technologies

Adopting advanced search methods—like vector search, semantic search, and hybrid models—can unlock meaningful gains across your organization. By removing friction from knowledge access, these tools elevate the way teams collaborate, execute, and make strategic decisions.

Faster, more accurate information retrieval

We’ve all been there—spending way too much time digging through files, chats, or tabs just to find that one piece of information. Smarter search significantly reduces wasted time. By combining lexical, semantic, and vector search methods, organizations can surface relevant answers faster—even when employees don’t know exactly what they’re looking for.

Improved decision-making

The right search tools surface deeper insights. Instead of re-creating work or chasing down links in Slack, teams can access summaries, comparisons, and contextual knowledge—leading to faster, more confident decisions.

Enhanced collaboration

By connecting content across apps and reducing knowledge silos, smart search tools support stronger collaboration. Teams get aligned faster when everyone is working from the same information.

Dropbox Dash: A smart solution for business search needs

Dropbox Dash is built for the way modern teams work—fast, cross-functional, and information-heavy. By combining advanced search techniques search in one AI-powered platform, Dash makes company knowledge more discoverable, useful, and secure.

AI-powered search with context-aware results

Dash understands what you mean, even if you don’t say it perfectly. Using advanced search techniques, it interprets intent and surfaces relevant content across your workspace. Whether you're looking for a “pricing model deck” or “last month’s onboarding plan,” Dash delivers what you need—even if the file name says something else.

Integration with the tools you already use

Dash connects with apps like Google Workspace, Slack, Dropbox, Salesforce, and more—so you can search across platforms without switching tabs. It brings all your scattered knowledge into a single, unified view.

Secure and efficient data management by desin

Dash keeps sensitive content protected with built-in encryption, role-based permissions, and permission-aware search. Users only see what they’re authorized to see—no manual setup required.

How to implement universal search in your business

Looking to upgrade your search experience? Here’s where to start:

1. Assess your needs

Start by identifying where your current search falls short. What tools does your team already use? Where is knowledge stored? What types of content—documents, decks, conversations—are hardest to find?

2. Choose the right platform

Look for a solution that combines lexical, semantic, and vector search. Dropbox Dash does all three—plus it integrates with the tools your team already relies on.

3. Connect your data sources

Sync Dash with your cloud storage (e.g., Dropbox, Google Drive), CRM (e.g., Salesforce), messaging tools (e.g., Slack), and other apps. The more connected your stack, the more powerful your search becomes.

4. Set permissions

Use role-based access controls to ensure team members only see what they’re authorized to. Dash mirrors your existing permission structures, so data stays protected by default.

5. Train your team

Help employees understand how to get the most from features like semantic search, smart summaries, and cross-app discovery. A short walkthrough can drive long-term adoption and success.

6. Review usage data and collect feedback

Are people finding what they need? Are certain tools or sources underutilized? Dash makes it easy to identify gaps and improve over time.

Unlock faster, smarter search with Dropbox Dash

Finding the right information shouldn’t slow your team down.

Whether you're navigating unstructured data, chasing down a specific file, or just trying to remember where that draft lives—Dropbox Dash combines vector, lexical, and semantic search to help you get there faster.

Built for modern workflows, Dash brings all your content together in one secure, searchable place. Explore Dropbox Dash today.