Many AI systems need to find every relevant items in a large collection and then organize what they found.
The standard way to do this is to tag each item with labels from a curated vocabulary and search on those labels.
Those labels are useful for organizing results, but searching on them misses items that do not fit a label and returns too much when labels are broad.
We compare this approach with an embedding-based approach: find candidates by meaning using text embeddings, then have a language model keep only the relevant ones.
In a corpus of 7,354 randomized trials, label search found 45% of the evidence a systematic review would include, while the new approach found 92%, at a cost per query that rises with search depth.
These results support a two-stage architecture that combines broad candidate retrieval with query-specific evidence filtering.