To further reduce the number of DFT computations, we applied our filtration method on the union of all 24,000 generated structures instead of the individual models outputs to again select only a few molecules to relax. Fig. (right) shows the clustered data for 20 clusters, where we again selected one structure per cluster to relax. Fig. (left) depicts the discovered structures as a function of the number of clusters, ranging from 14 to 50. We find that as few as 15 relaxations can already discover 12 isomers, and all 14 isomers are discovered with at least 38 relaxations. Interestingly, two specific structures are rarely found, namely CC(C)CCO and CCC(O)CC. The structure CCC(O)CC is highlighted in Fig. (right), and shares a cluster with another target structure. Similarly, CC(C)CCO often shares a cluster with another target structure depending on the number of clusters. Since only one structure per cluster is relaxed, this makes them difficult to discover and necessitates a large N cluster . Nevertheless, by relaxation of only 38 structures we are able to rediscover all possible C 5 H 12 O isomers, showcasing the good performance of both our generator model and filtering method, as well as highlighting that the developed architecture is capable to easily predict systems at different stoichiometries as the training data. This is in contrast to other generative approaches where the whole structure is evaluated by the critic or decoder, corresponding to a molecular descriptor similar to the left panel in Fig. .