Computer-aided drug design (CADD) has become an even more active research field with the rise of deep learning . The cooperation of researchers from various backgrounds ranging from organic chemistry to computer science is required to design feasible new compounds; however, it is not always easy to combine multidisciplinary insights. In recent years, there have been plenty of researches on molecular generative models. Still, some of these researches only look at numerical performance evaluations and lack the discussions about chemical perspectives of generated compounds. Extracting candidates of true value for drug development from computationally generated molecules requires a multifaceted evaluation. .