Recovering a chemical reaction network (CRN) from concentration data can be framed as program synthesis, but the search scales poorly: reaching the ground-truth network requires enumerating a very large number of candidates. Hard constraints on the synthesizer's grammar prune candidates by chemical rules such as atom valence and mass balance, yet among the valid candidates a uniform search has no sense of which reactions are plausible. We ask where in a top-down CRN synthesizer knowledge from a reaction database most reduces the candidates explored before the target is found? We compare three integration points across five benchmarks, using the USPTO-50K and Rhea databases: (1) a database-derived building-block vocabulary, (2) a probabilistic context-free grammar (PCFG) over the grammar rules, and (3) an output reranker. The building-block vocabulary is the most positive result: it decides whether the target network is recovered at all, and a shuffle control attributes this to the database's frequency content rather than vocabulary size. Each corpus unlocks only the chemistry it covers, and rank-normalised merging recovers targets that a naive union dilutes. The PCFG adds ordering gain, while the reranker, acting only on the finished list, helps under corpus match and hurts under mismatch. Matching a corpus to the target chemistry, not enlarging it, is what turns a reaction database into useful search guidance.

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