We evaluated S4 for its ability to learn elements of bioactivity. With CLMs this is often achieved with transfer learning , which allows transferring knowledge acquired from one task to another task with fewer available data. Via transfer learning, after pre-training a CLM on a large corpus of SMILES strings, the model can be then 'fine-tuned' on a smaller, and task-focused set (e.g., bioactive molecules) by additional training . Here, we performed five fine-tuning campaigns, focusing on distinct macromolecular targets from the LIT-PCBA dataset: (1) pyruvate kinase muscle isoform 2 (PKM2), (2) mitogen-activated protein kinase 1 (MAPK1), ( ) glucocerebrosidrase (GBA), (4) mechanistic target of rapamycin (mTORC1), and (5) cellular tumor antigen p53 (TP53).