[GTM glossary](/glossary.txt) Semantic search

Contextual Intelligence & AI

# Semantic search

In short

Semantic search is a search method that matches the meaning of a query to the meaning of content, instead of looking only for the same words. A search for "firms that sell payroll software" can return a company described as "HR and compensation platform".

It usually works by turning text into embeddings, which are numeric representations of meaning, and finding the closest matches. This makes it useful when people describe things in different words.

## Semantic search vs. keyword search

* **Keyword search:** Matches the exact terms typed. Fast and precise, but misses synonyms and different phrasing.
* **Semantic search:** Matches concepts, so synonyms and descriptions still return relevant results, though results can be less predictable.
* **Hybrid:** Many systems combine both, using keywords for exact names and semantic matching for concepts.

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## Semantic search in GTM

* **Company discovery:** Describe a target in plain language and find companies that match, even if they never use your wording.
* **Lookalike lists:** Find accounts that resemble your best customers by what they do.
* **Knowledge search:** Help reps find the right playbook or case study by asking a question.
* **Content and email search:** Locate past messages or notes by topic rather than by remembered phrase.

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## A practical example

Say a rep searches for "companies selling to restaurants". A keyword search might only return pages with that exact phrase. A semantic search can also surface a point-of-sale vendor and a food-ordering app, because their descriptions are close in meaning.

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## Common mistakes

* **Expecting exact matches:** For a specific name or ID, keyword search is more reliable.
* **Skipping evaluation:** Test with real queries and check whether top results make sense.
* **Poor source text:** Thin or vague company descriptions give weak embeddings.

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## Semantic search FAQs

**What is semantic search?**

Semantic search finds results by matching the meaning and intent of a query rather than exact words. It converts text into numeric representations of meaning and returns the closest matches, so synonyms and different phrasing still produce relevant results.

**What is the difference between semantic search and keyword search?**

Keyword search matches the exact terms you type. Semantic search matches meaning, so it can return results using different words. Keyword search is better for exact names and codes, and many tools combine both approaches.

**How is semantic search used in B2B prospecting?**

It lets teams describe a target market in plain language and find companies whose descriptions match the idea, even without shared keywords. It also helps find lookalike accounts and surface relevant internal documents.

**Does semantic search use AI?**

Yes. It relies on machine learning models that create embeddings, which capture the meaning of text. These embeddings are compared to find similar content, which is what allows it to understand related terms and intent.

Share term

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## More related phrases

* [Company embeddings](/glossary/company-embeddings.txt)
* [Knowledge graph](/glossary/knowledge-graph.txt)
* [Ontology](/glossary/ontology.txt)

## Similar phrases

* [Natural language search](/glossary/natural-language-search.txt)  
Natural language search lets you find companies, contacts, or records by typing a plain sentence, such as "fintech startups in Germany hiring sales reps", instead of setting filters by hand.
* [Embeddings](/glossary/embeddings.txt)  
Embeddings are numeric representations of text, companies, or other items that place similar things close together, enabling semantic search, matching, and lookalike discovery.
* [Vector search](/glossary/vector-search.txt)  
Vector search finds items by comparing numeric embeddings and returning those closest to the query, which lets systems match on meaning, similarity, or other features rather than exact keywords.
* [Search intent](/glossary/search-intent.txt)  
Search intent is the goal behind a search query, such as learning, comparing, or buying. B2B teams use it to match content to what searchers need.
* [Lookalike search](/glossary/lookalike-search.txt)  
Lookalike search is a prospecting method where you start from one or more example companies and search a database for others with similar firmographic, technographic, or content traits.