For a proof of concept, some of the molecules generated by the CLM were tested for PI3Kγ binding in vitro. To optimize the efficiency in terms of both time and resources, we selected the test molecules from the refined virtual chemical libraries that could be purchased from commercial suppliers, as opposed to synthesizing the de novo designs. In total, 16 computer-generated molecules were commercially available. Their predictive confidence ranged from 80/100 votes for compound 1 to 24/100 votes for compound 16 (Fig. ). Although none of the ordered molecules was part of the top-ranked set (i.e., receiving 99/100 votes), compound 1, the molecule with the highest number of votes (80/100), was a hit, with Kd ranging between 0.6 and 0.7 µM (n = 2; 670 nM and 620 nM) (Figs. and, and Supplementary Table ). None of the lower-ranking compounds inhibited PI3Kγ in the biochemical assay (Fig. ). The confidence level of our ensemble correctly prioritized compound 1 (active in vitro) over compounds 2 and 4 (inactive in vitro), despite all of them having the same scaffold but with different substituents (Fig. ). We hypothesize that this might be due to the positive effect of the ELECTRA pretraining, which was aimed at recognizing the effect of small structural changes. Hit compound 1 has a new atom scaffold compared to all molecules in the ChEMBL database (version 28) annotated with "pActivity" ≥ 5.0 on PI3Kγ ("pActivity": -log(molar IC50, XC50, EC50, AC50, Ki, Kd, or "potency")). The most similar molecule among these has a Tanimoto similarity of 34% to that of compound 1 (Supplementary Fig. ). This preliminary in vitro validation advocates an ensemble prediction approach for virtual compound screening and ranking of the computer-generated molecular designs.