Except for one target (PIM1), in all other cases at least two augmentation strategies perform better than SMILES enumeration when it comes to generating diverse and novel molecular scaffolds (Table ). Token deletion performs on average the best, regardless of the molecular similarity, the number of fine-tuning molecules and the macromolecular target. These results are owed to the nature of the approach, which perturbs the input by generating diverse SMILES for training (1%-24% scaffold novelty in the training set, Supporting Table ). The other methods based on input 'perturbation' (bioisosteric substitution, self-training, Supporting Table ) also often show top performances across targets. In general, the performance of the other augmentation methods in comparison with SMILES enumeration depends on the considered target and fine-tuning scenario.