Our results show the distinct advantages of each augmentation strategy, and the potential of atom masking as a good alternative to SMILES enumeration in particularly low-data scenarios, for distribution learning and design of structurally diverse candidates. Ultimately, our work equips machine learning practitioners with a broader computational toolkit for chemical space exploration with CLMs.