Figure shows the same distributions of Figure but for a different setup, where we considered the PubChem training set, the 2,685 ChEMBL-series compounds and 26,850,000 sampled compounds. The sampled compounds are generated with beam size, B = 10, 000 by feeding the transformer model (trained with ECFP4 with counts and λ = 10) with the 2,685 ChEMBL-series compounds. We notice that the distributions of the sampled compounds this time agree with the distributions of the ChEMBL-compounds. This denotes the ability of the model of not being biased towards the PubChem dataset used to train the model. For example, the ChEMBL-series compounds had an average QED of 0.55 and the sampled compounds have similar QED average. Also the molecular weight is better aligned with the ChEMBL-series molecular weight rather than the PubChem molecular weight. Finally, 99.92% of the valid and unique sampled compounds did not did not appear in PubChem nor ChEMBL-series, remarking the ability of the model of generating novel compounds.