For in-house projects, only the generative models for project A and B could find identical compounds to the real high activity compounds. The percentage of rediscovery in project A and B were 0.10%, and 0.15% for all 5,000 generated compounds; when using in silico classification scores for further selection 0.20% and 0.00% of the top 500 scored generated compounds represent known actives, while the top 100 scored generated compounds had no rediscovery (more details are shown in Table ). This decrease of rediscovery and lack of enrichment achieved by additional ranking of in silico classification model indicates that prospective performance may be poor, and indeed in the case of project B retrospective performance was marginally better than random (Figure ). Hence, we consistently find that rediscovery was much higher for public projects than in-house projects. Rediscovery was less than 1% for all generated compounds, and less than 3% for the top 100 scored compounds (Fig. ), which is significantly lower than in a previous study: In work by Segler et. al. where the rediscovery ratio was around 10% for two bioactivity endpoints (which were growth inhibition endpoints though, namely inhibitory activity for Plasmodium falciparum and Staphylococcus aureus). However, methodological differences exist: In this previous study the test dataset was selected randomly and removed from the training dataset, which means that congeneric compounds might still exist in the training dataset, and then the generative models were fine-tuned. This explains the high rediscovery rates; however, this situation doesn't really resemble a real-world drug discovery situation. On the other hand, in a study performed by Atance et. al. , which removed a test dataset for DRD2 completely from the training dataset, the percentage of rediscovery was less than 1%; the condition of this study was more similar to ours with respect to conditions and results obtained.