For prospective evaluation, we applied the beam search to the design of natural-product inspired RORγ ligands. Learning from natural products as a traditional source of inspiration for drug discovery may hold several advantages over learning from purely synthetic molecules, because of the overall higher structural diversity, greater threedimensionality, and often superior selectivity of bioactive natural products . We aimed to obtain de novo designs possessing three properties: (i) natural product inspired chemical structure, (ii) synthesizability, and (iii) bioactivity on RORγ. Aiming to fulfil all three objectives during transfer learning, the CLM that was previously pre-trained on bioactive molecules from ChEMBL 15 was fine-tuned on one synthetic and four natural product RORγ modulators (Suppl. Fig. ). Beam search sampling was started after the fifth epoch of fine-tuning, to ensure that the CLM had sufficiently captured the molecular features of the small fine-tuning set.