The current trend in chemoinformatics is the usage of deep learning architectures to learn the structural features of molecules. Deep learning architectures attracted much attention as they can conditionally generate chemical structures based on the learned features. While it is an active field of research, many works showed possible advantages of deep learning in constrained molecular optimisation and virtual libraries generation . Nevertheless, few studies have explored the properties of the learned molecular representations.