The chemical universe of drug-like molecules is incredibly vast, making the discovery of new medicinal drugs with traditional approaches a daunting task. Generative deep learning has gained remarkable attention due to its ability to generate molecules on-demand with desirable properties. Notably, chemical language models 2 (CLMs) have shown their potential to learn complex molecular properties, and have been applied to numerous wet-lab studies for bioactive ligand design. CLMs adapt algorithms from natural language processing (NLP) to learn the 'chemical language', and generate molecules in the form of strings with desired properties. Simplified Molecular Input Line Entry Systems (SMILES) strings are one of the most widely used line notations for CLMs, among others. SMILES strings represent two-dimensional molecular information in the form of text (Fig. ), by traversing the molecular graph and annotating (topo)chemical information with dedicated characters ('tokens') that represent atoms, bond, rings, and branches. SMILES are non-univocal: the same molecule can be represented with different SMILES strings, depending on the starting atom and the chosen graph traversal path (Fig. ). Such non-univocity becomes beneficial to achieve data augmentation, i.e., to artificially inflate the number of samples available for training 'data-hungry' CLMs. Via SMILES enumeration (also referred to as 'randomization' ), a molecule is represented by several, different, SMILES strings during training. SMILES enumeration yields beneficial effects on the quality of the de novo drug designs, especially in low-data scenarios. Moreover, SMILES enumeration has improved model quality in various other chemistry tasks, e.g., organic synthesis planning, bioactivity prediction, and supramolecular chemistry. Inspired by the impact of SMILES enumeration, we introduce additional augmentation strategies to further stretch the boundaries of chemical language modelling. By combining augmentation techniques inspired by NLP with chemistry insights, here we introduce for the first time four SMILES augmentation strategies into de novo design: (a) token deletion, whereby specific tokens are removed from a SMILES string; (b) atom masking, which replaces specific atoms with a placeholder token; (c) bioisosteric substitution, which replaces functional groups with their corresponding bioisosteres, and (d) self-training, where SMILES strings generated by a CLM are used as input for the next training phase. These approaches, in several variants, were systematically compared to SMILES enumeration, with varying training set sizes and in combination with transfer learning.