In chemical language modelling, SMILES enumeration has showed incredible results for data augmentation. In this work, we rethink how SMILES strings can be augmented for de novo design with chemical language models. In particular, we introduce four augmentation strategies (and several variants) and systematically analyse their ability to generate molecules with desirable properties and relevant molecular scaffolds. This systematic study shed light into the different advantages and unique features of each augmentation strategy.