This is specially the case with canonical SMILES: Morgan numbering greatly reduces the possible paths, as it tends to prioritize starting in sidechains rather than in the rings of the molecule . This makes sense when grammatically simpler SMILES strings are desired. We think that by using randomized SMILES, models become more action-based rather than grammar-based. Additionally, this may also indicate why the syntax changes added in DeepSMILES have a detrimental effect on the learning capability of SMILES generative models, as they give the model a more complex action space. For instance, the ring token altered behavior makes the ring closures extremely grammar sensitive and the new branching token behavior makes the SMILES strings unnecessarily longer without any appreciable improvement. We think that the SMILES syntax is, with all its peculiarities, an excellent hybrid between action-based and grammar-based generative models and is, to our knowledge, the most successful molecular descriptor for deep learning based molecular generation available so far.