We analysed the performance of each method across data size scenarios, focusing on the ability to learn the 'chemical syntax' of the SMILES language and the physico-chemical properties of the training set. For each augmentation strategy, we trained CLMs using (a) three levels of probability of perturbation (p = 0.05, p = 0.15 and p = 0.30) for token deletion, atom masking, and bioisosteric substitution; (b) four levels of augmentation, i.e., one-fold (no augmentation), three-, five-and ten-fold augmentation (corresponding to using three, five, and ten times more SMILES than the original training set size, respectively); and (c) six training sets extracted from ChEMBL, and containing different numbers of molecules (1000, 2500, 5000, 7500, 10000, and 25000 molecules). Enumeration was used as a baseline to benchmark the potential of the new augmentation strategies; for this method, ten-fold augmentation was used based on its performance (Supporting Fig. ). For each setup, a CLM was trained on the (augmented) set and used to generate 1000 SMILES across four repeats (4000 generated strings in total).