Additionally, to investigate from another perspective of the difference of the result in the public and in-house dataset, we calculated NLL of real compounds located in α, β and γ with the prior network, the agent in FL, and the agent in RL. NLL reflects how likely the compound is to be sampled from the model (prior or agent); if the value is small, the compounds are more likely to be sampled from the model, and vice versa . The result is shown in Figure , where it can be seen that in any dataset (public/in-house) and area (α, β and γ) the NLLs of the agent in FL were smaller than that of the agent in RL. This suggests that the FL was at least most likely to rediscover molecules from these subsets, however, in practice rediscovery rates were still very low. Focusing on the NLL of the agent in RL, in the public dataset (Figure to e), the NLLs in γ were comparable to β; and in the in-house dataset (Figure to k) the NLLs in γ slightly higher than that in β in project B, D, and F. This likely also reflects the observation of lower empirical classification scores for γ in these projects. Hence, we can conclude that compared with in-house dataset, in silico classification model of public dataset and therefore RL works better, and this reflects the better result of rediscovery and aSNN in public dataset than in in-house dataset. If the number of α is not zero and that of γ is more than that of β, this seemed to be predictive of classification accuracy for the generative model (light grey column). On the other hand, if the number of α is not zero and that of γ is less than that of β, this seemed to be unpredictive (dark grey column).