These findings show a first important benefit of the beam search sampling technique. Beam search sampling reveals the most likely designs from a CLM which can be assessed regarding their compliance with the design objectives to evaluate model performance and the success of fine-tuning. Aiming to improve upon these results, we performed a second experiment, in which we applied a stepwise fine-tuning strategy. First, the pre-trained model was fine-tuned for 20 epochs on 255 synthetic RORγ ligands from the US patent subset of the Protein Data Bank 38 (255 molecules, Suppl. Table ) to capture both bioactivity and synthesizability. Then, the model was fine-tuned with four natural product RORγ modulators (Suppl. Fig. ) for 16 epochs, aiming to bias the model towards natural-product-likeness. With this second approach, the top 5 sampled molecules (Figure ) were synthetically accessible according to IBM RXN , which could propose a synthetic route for each of them. Importantly, the computer-generated molecules possess certain natural product characteristics (Fig. , Suppl. Table ), as indicated by a high fraction of sp -hybridized carbon atoms (Fsp 3 ). The top five designs have Fsp 3 values ranging from 50% to 75%. These values are comparable to the values computed for the MEGx natural product library (Analyticon Discovery GmbH, rel. 09-01-2018), and exceed the average Fsp 3 content of the ChEMBL molecules used for pre-training (51±30% and 33±20%, respectively). These data suggested that the two-step fine-tuning procedure complied with the design objectives. The two-step approach was chosen for prospective application.