Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, chemical screening assays evaluate the functional responses of candidate compounds against disease targets. Unstructured text that describes the biological mechanisms through which these targets operate, experimental screening protocols, and other attributes of assays offer rich information for new drug discovery campaigns, but has been untapped because of that unstructured format. We present Assay2Mol, a large language model-based workflow that can capitalize on the vast existing biochemical screening assays for early-stage drug discovery. Assay2Mol retrieves existing assay records involving targets similar to the new target and generates candidate compounds using in-context learning with the retrieved assay screening data. Assay2Mol outperforms recent machine learning approaches that generate candidate ligand compounds for target protein structures, while also promoting more synthesizable molecule generation.