Evaluating the bioactivity of de novo designs (besides synthesis and wet-lab testing) is non-trivial, since this property cannot be fully captured by traditional molecular descriptors, and might not be accurately predicted by quantitative structureactivity relationship models . Hence, we used experimentally-tested molecules to evaluate the capacity of a CLM to learn elements of bioactivity retrospectively. Several studies have shown that the likelihoods learned by a CLM during fine-tuning can be used to prioritize designs with high chances of being bioactive . Based on the same principle, here we used the likelihoods learned by the CLMs to rank existing molecules and evaluate their capacity to prioritize bioactive compounds over inactive ones.