According to the investigation we performed so far, the generated compounds for the public dataset had better rediscovery ratio and aSNN than the in-house dataset (Fig. , Fig. ). Firstly, we investigated the real activity of each compound, and concluded that the public dataset which was transformed on a pseudo-time axis has an explicit relationship between activity and pseudo time; however, for the in-house dataset this relationship was much less profound (Fig. ). In order to investigate this difference from the viewpoint of each compound's topology and predictivity for late-stage active compounds, we further analysed the public and in-house dataset by counting the compound's number in each region α, β, and γ after clustering by kmeans. This analysis was meant to show whether each project had predictive or unpredictive clusters for the generative model. Specifically, if the number of compounds coming from α is not zero (i.e., active early stage compounds are contained in a given cluster) and the number of compounds from γ (i.e., late-stage active compounds) is higher than the number of compounds from β (i.e. late-stage inactive compounds), this is a 'predictive' cluster for the generative model, in the sense that the chemical space of the late-stage, desirable compounds is present in the early stage for the generative model (and vice versa for 'unpredictive' clusters). Table shows the results of this analysis. It can be seen that for the public projects, most projects had more predictive clusters than unpredictive (the numbers of clusters which predictive/unpredictive in DRD2 was 7/1, for GSK this was: 4/4, for CDK2 4/3, for EGFR 5/4, and for ADRB2 5/5). On the other hand, in in-house projects, all of the models had more unpredictive clusters than predictive (the numbers of cluster predictive/unpredictive in project A was 0/6, in project B 1/5, in project C 1/6, in project D 0/5, in project E 0 /7, and in project F 1/3; Table ). Even in the projects which have predictive clusters, the number of contents were highly biased; the compound's number in each region α/β/γ for B of predictive cluster ID 6 were 112/0/2, those for C of predictive cluster ID 8 were 1/20/82, and those for F of predictive cluster ID 2 were 26/0/1. Based on this result, we conclude that the difference of rediscovery ratio and aSNN between the public and in-house projects might be based on whether projects have predictive clusters (and to which extent this is the case), or not. In this sense, we can formulate as a success criterion to utilize generative models in the drug discovery process is that we require seeds of promising compounds in the training dataset. Active learning might be a stepwise approach here. It should also be mentioned that the evaluation performed here is based on known actives only -hence, we were not able to evaluate the quality of potential 'false positive' compounds. We should note that this study focused on the ligand-based approach. Another method, that is free from QSAR modeling, is a structure-based approach, and recently there has been more research in this approach combined with generative models; see the references in detail .