Computational methods have become key players in hit and lead discovery in pharmaceutical research, complementing experimental high-throughput screening . Bespoke virtual compound libraries provide access to untapped regions of the chemical space 2 , thereby extending the diversity of potential drug candidates. However, owing to the potentially unlimited size of virtual chemical libraries, concerns have been raised over the pragmatism of successfully screening billions of molecules virtually with a potentially high risk of false positives . To mitigate some of these challenges, researchers have employed generative deep learning models to construct compounds on demand by de novo design and to obtain small, bespoke virtual compound libraries . A variety of data-driven approaches can be used to generate focused virtual chemical libraries and create molecules with the desired properties . Chemical language models (CLMs) are based on deep learning networks for processing string representations of molecules (e.g., simplified molecular input line entry system (SMILES) strings; Fig. ) . CLMs have already been successfully employed to generate focused virtual chemical libraries. Examples of de novo designed bioactive molecules include inhibitors of vascular endothelial growth factor receptor 2 kinase and the unfolded protein response pathway , and nuclear hormone receptor modulators .