What Is Semantic Search and Why Does It Matter for Deal Sourcing?

Semantic search is a method of information retrieval that matches based on meaning rather than exact words. Instead of checking whether a document contains the terms you typed, it converts both your query and every document into numerical representations called vector embeddings, then finds the documents whose meaning is closest to yours. For deal sourcing, this is a fundamental shift. Companies describe themselves in their own language, not yours, and semantic search is the first approach that actually handles that.

How does semantic search work, technically?

The core idea is simple. A machine learning model reads a piece of text — a company description, a query, anything — and converts it into a vector: a list of numbers (typically 768 or 1,536 dimensions) that encodes what the text means. Words that appear in different contexts get different representations. Phrases that mean the same thing end up with similar vectors even if they share no words.

Here's the process applied to deal sourcing:

1. Every company description in the database is converted into a vector embedding and stored.
2. When you search, your query is converted into a vector using the same model.
3. The system computes the cosine similarity between your query vector and every company vector, measuring how close they are in meaning.
4. The companies with the highest similarity scores are returned as results.

The key difference from keyword search is that step three doesn't look for word overlap. It looks for meaning overlap. "Industrial automation software" and "workflow digitization for discrete manufacturers" produce vectors that are close to each other because the underlying concepts are similar, even though the phrases share zero words.

Why does this matter for finding private companies?

Public companies have standardized SIC codes, well-known brands, and analyst coverage that makes them easy to categorize. Private companies have none of that. A Series B company building AI-driven quality inspection for food manufacturing might describe itself as "computer vision for production line compliance." Another might call itself "automated defect detection for CPG processors." A third might say "visual intelligence platform for food safety."

All three are effectively the same business. Keyword search treats them as completely different because the words are different. Semantic search recognizes the underlying similarity because the vectors are close.

This is the core problem in deal sourcing. The universe of private companies is described in inconsistent, idiosyncratic language. The better the company, the more likely it has a differentiated way of describing what it does — which is exactly what makes it invisible to keyword search.

How is semantic search different from just using better keywords?

A common workaround for keyword limitations is to run many searches with different phrasings. Search for "industrial automation," then "manufacturing software," then "factory digitization," and manually combine the results. Some platforms call this "Boolean search" and treat it as a feature.

The problem is that you can only think of the phrasings you already know. You can't anticipate how a founder in Dayton, Ohio chose to describe their workflow optimization platform. Semantic search doesn't require you to anticipate every variation because it operates on meaning, not terminology. One well-phrased query can surface companies across dozens of different phrasings.

There's also a precision difference. Keyword search for "automation" returns every company that uses that word anywhere in its profile — including marketing automation, home automation, and test automation companies that have nothing to do with your thesis. Semantic search understands that "industrial automation software for discrete manufacturers" is a specific concept and ranks results by relevance to that full meaning, not just the presence of individual words.

How does personalization layer on top of semantic search?

Semantic search on its own returns the same results for every user who types the same query. That's a good starting point, but deal sourcing is inherently personal. Two firms searching for "healthcare IT" might have completely different conceptions of what a good target looks like based on their portfolio, check size, and sector focus.

This is where hybrid scoring becomes important. A system can combine the semantic similarity score with additional signals — proximity to a firm's existing portfolio companies in vector space, alignment with stated investment criteria, geography and size preferences — to produce a personalized ranking. The semantic search provides the foundation of relevant companies. The personalization layer reorders them based on fit for a specific firm.

Radar works this way. It runs semantic search across its full company database, then applies a hybrid score that incorporates a firm's portfolio, thesis, and preferences. Two firms searching for the same thing see different orderings because the system understands that relevance is contextual, not absolute.

What should I look for in a semantic search tool for deal sourcing?

Not all implementations are equal. A few things matter:

Embedding quality. The vector model determines how well meaning is captured. Models trained on general web text perform differently than models tuned for business descriptions and company profiles. The best systems use embeddings specifically optimized for the language companies use to describe themselves.

Database coverage. Semantic search is only as good as the data it searches over. A perfect algorithm searching a thin database still misses companies. Coverage of private companies — especially earlier-stage and non-US companies — varies enormously across platforms.

Query interface. Semantic search works best when you can describe what you're looking for in natural language rather than constructing filters. The query "B2B SaaS companies selling compliance tools to mid-market financial services firms" should work as a single input, not require five separate filter selections.

Transparency. You should be able to understand why a company appeared in your results. Systems that show similarity scores or explain the match give you confidence in the output and help you refine subsequent searches.

Radar is built on semantic search from the ground up — it's the core of how the platform finds companies. Rather than starting with filters and adding a semantic layer, it starts with meaning-based retrieval and lets you add structured filters on top when you need them.

Does semantic search replace human judgment in deal sourcing?

No. It changes what human judgment is applied to.

With keyword search, a significant portion of sourcing effort goes into constructing the right queries, running multiple searches, deduplicating results, and manually reviewing long lists of loosely relevant companies. The judgment is spent on search mechanics.

With semantic search, the initial list is already filtered by meaning. The judgment shifts to evaluating companies that are genuinely relevant — reading their descriptions, assessing fit, deciding who to reach out to. That's a better use of a dealmaker's time.

The firms that source effectively aren't the ones that skip human judgment. They're the ones that apply it to a better starting set. Semantic search is how you get that starting set.

Radar uses semantic search to find companies by what they do, not what keywords they happen to use. Describe your thesis and see what you've been missing.