The same benchmark study was also performed on models with a drug-like training set from ChEMBL (see Suppl. Methods for more information on how the training set was obtained). A different and reduced set of hyperparameter values were used due to long training times (Table ). The best models for both the canonical and restricted randomized SMILES benchmarks were obtained using the same procedure as before and a 2 billion sample was performed. Results show (Table , extended results Suppl. To prove that the molecules sampled from the randomized SMILES model are at least as diverse as those in the canonical, several physicochemical properties and metrics (as used in the MOSES benchmark ), such as molecular weight, logP, Synthetic Accessibility Score (SA) , Quantitative Estimate of Drug-likeness Score (QED) , Natural-Product likeness score (NP) and Internal Diversity (cross-molecule Tanimoto similarity on ECFP4) were calculated for a sample of the training, validation, randomized SMILES model and canonical SMILES model (Suppl. Figure ). All of the plots are nearly identical, showing that there is no clear difference between molecules in any of the four sets. Additionally, molecule NLL plots for the same four samples were calculated for both models (Figure ) and show that the canonical model greatly overfits the training and validation sets compared to the randomized SMILES model, which has mostly the same distribution for both sets. When comparing the two samples, the canonical model has much lower probabilities of generating most of the molecules generated by the randomized SMILES model, but not the opposite. The randomized SMILES model is able to generate the canonical SMILES model molecules higher likelihood than average, implying that the output domain of the canonical SMILES model is a subset of the randomized SMILES model output domain.