Our study reveals that some of these methods can advance chemical language modelling further in comparison with the well-established SMILES enumeration. No augmentation strategy resulted able to 'rule them all', but the optimal approach depends on the overall goal. When training from scratch with small datasets (e.g., less than 5000 training molecules), different augmentation methods allow to match different physico-chemical properties differently. In this context, bioisosteric replacement, self-training and atom masking resulted particularly interesting alternatives to SMILES enumeration, depending on the property of interest. When combined with transfer learning, atom masking confirmed its potential to outperform the distribution matching capabilities and scaffold matching of SMILES enumeration, especially with low-data regimes (i.e., 10 fine-tuning molecules) or fine-tuning sets composed of highly similar molecules. The other augmentation strategies showed a task-dependent performance. When it comes to navigating the chemical space in search for diverse molecules, strategies that perturb the input SMILES for augmentation (e.g., token deletion and bioisosteric substitution) show the highest potential to provide novel scaffolds. These results underscore the opportunities of these new augmentation strategies to further accelerate experimental de novo design campaign. We expect each one of these techniques to be better suited for chemical space exploration (e.g., bioisosteric replacement and token deletion) or library enlargement (e.g., atom masking). While our study only focused on SMILES strings, its results can be applied to virtually any molecular line notation, such as (Group)SELFIES, 12,46 fragSMILES 12,46,47 and SAFE. Moreover, while here we focused on distribution learning, these newly-introduced augmentation techniques are expected to support other learning regimes, such as reinforcement learning. In this context, we expect approaches that allow a higher diversity of molecular designs (e.g., token deletion and bioisosteric replacement) to be particularly beneficial to explore uncharted regions in the chemical space, steered by model rewards. Finally, the approaches presented here are easy to expand upon based on the user needs (e.g., by specifying a different set of functional groups to be considered/replaced for masking and bioisosteric substitution) and are hence expected to show additional potential in the future.