We then compared the beam search designs obtained with the chosen computational strategy to known RORγ modulators and to the fine-tuning molecules (Fig. ). Despite being a "greedy" algorithm, the beam search sampling still allowed to explore the chemical space beyond the regions that are populated by the fine-tuning compounds (Figure ). Compared to the inverse RORγ agonists annotated in ChEMBL (IC50 <1 µM, Figure ), the beam search designs are structurally more diverse in terms of substructure fragments encoded by Morgan fingerprints . The designs possess a certain degree of similarity to the known active molecules in terms of their three-dimensional shape and partial charge distribution (WHALES descriptors ). Apparently, the CLM, on top of learning the SMILES "syntax", also learned certain "semantic" ligand features that are relevant for binding to macromolecules, such as their molecular shape and partial charge patterns.