Generative deep learning can be applied to design pharmacologically active compounds de novo . Deep learning-based molecular design algorithms can extract high-level molecular features from "raw" molecular representations , such as molecular graphs and the Simplified Molecular Input Line Entry System (SMILES) , potentially allowing them to access unexplored regions of the chemical space . Previous studies demonstrated that "chemical language models" (CLMs) that were trained on SMILES strings can generate novel molecules with experimentally validated bioactivity . CLMs have shown the ability to learn focused chemical features from small collections of template molecules thanks to transfer learning which reuses previously learned knowledge on a new task . A CLM is first pre-trained on a large amount of unlabeled data to capture the SMILES 'syntax' (i.e., how alphanumeric characters should be assembled to generate strings that correspond to valid molecules) and the physicochemical properties of the pre-training dataset. Then, the pre-trained CLM is "finetuned" with a smaller set of task-specific data . During this transfer learning process the CLM is biased towards the chemical space of interest, that is, molecules with desired biological and physicochemical properties. This "few-shot" learning ability renders CLMs particularly useful for application to biological targets, for which only few ligands are known.