To investigate whether the generative model can produce compounds similar to known actives, we next calculated the aSNN (average Similarity of the Nearest Neighbour) between the generated compounds and known active compounds. The aSNN between generated compounds and the real compounds which belong to the middle stage are shown in Fig. to c (all the value of aSNN can be seen in Table ). For the public projects, given all of 5,000 generated compounds, aSNN through the projects of high/ultra-high activity compounds were much higher than that of low/middle activity compounds (the average of aSNN across projects for low/middle/high/ultra-high activity was 0.304/0.367/0.420/0.408, respectively) . Hence, for the public dataset, and given the particular way this dataset was constructed, optimization towards the single objective of primary target activity was possible. For the in-house projects those trends were inconsistent (the average of aSNN across projects for low/middle/high/ultrahigh activity was 0.431/0.425/0.427/0.348, respectively). For project A and B the aSNN to high activity compounds was higher than the corresponding value for low/middle activity compounds; however, for projects C to F the aSNN of generated compounds to high/ultrahigh compounds was conversely lower than that to low/middle activity compounds (Fig. ).