In addition to the binding properties of the generated molecules, we also look at two additional factors, quantitative estimate of drug-likeliness (QED) (Bickerton et al., 2012) and synthetic accessibility (SA) . QED is a quantitative score that asses the quality of a molecule through comparisons of its physicochemical properties to approved drugs. SA is a score that accounts for the complexity of the molecule in the context of easiness of synthesis, thereby providing an auxillary metric for the feasibility of the compound as a drug candidate. Single property optimization is often a flawed task, because the generator can overfit to the pretrained predictor and generate unsynthesizable compounds. While we report our results on both single-property and multi-property optimization tasks, we focus our analysis on the hardest multi-objective optimization task: GSK3β+JNK3+QED+SA.