This makes benchmarking models much more complex. For instance, a model could generate an extremely diverse set of molecules, most of which are completely unrelated to the training set chemical space, compared to a model that generates less diverse and fewer molecules that are more akin to the training set chemical space. As it is unknown which is the target chemical space, assessing which is the best model is impossible. For this reason, some methods were published that aggregate a set of metrics to obtain a better overview of the output domain of the model. Unfortunately, they compare the models with a test set split from the training set and this tends to benefit models that overfit. This is the case with ChEMBL models trained here, as those trained with randomized SMILES have a much larger output domain (at least double) than those trained with canonical SMILES. Moreover, sets of molecules generated are physicochemically almost indistinguishable (Suppl. Figure ) from sets generated from the canonical SMILES model. Nevertheless, the FCD is significantly lower in the canonical SMILES model than in the randomized SMILES one (Table ). This means that the FCD is either biased towards the canonical SMILES model (and to the training set) or that the additional molecules generated by the random model are of less interest than those generated by the canonical. We think that from the results obtained in the GDB-13 models, these molecules are from the target chemical space. To be able to have a better answer, further research should be made in obtaining better and more unbiased sets to train and test generative models on.