As a framework to probe beam search sampling, we employed a recently published CLM based on a recurrent neural network with long short-term memory (LSTM) cells. The CLM was trained on 365,063 molecules from ChEMBL , encoded as SMILES strings (Fig. ), to iteratively predict the next character of each SMILES string, given the preceding tokens (Fig. ). This pre-trained CLM was then biased towards the design objective, i.e., the generation of new molecules with bioactivity on RORs, by transfer learning using collections of known ROR ligands (Supplementary Figure , Supplementary Table ).