Despite the results obtained by previous research, the question as to how much of the chemical space surrounding the molecules in the training set can be generated by a RNN trained with SMILES remains unanswered. The Fréchet ChemblNet distance, which compares a given generated chemical library with real molecule data from ChEMBL was recently proposed as a benchmark. However, the ChEMBL database has two main issues. First, it has a strong bias, as it only contains molecules obtained from literature. Second, it is very sparse, spanning a large chemical space with roughly 1.5 million molecules. The SMILES notation has also a known ambiguity: each molecule can have multiple different SMILES that uniquely represent it.