However, in a practical drug discovery process, goal-directed optimization is much more important. The gold standard of measuring model performance would be to synthesize and test de novo molecules experimentally (and compare to a baseline control ); however, this is intractable for all models considering the experimental resource requirement, given the number of models available and the number of de novo molecules proposed 2 . Recently the CACHE initiative started, whose aim is to validate computationally suggested or generated compounds by experimental testing; however, this activity is limited in scope due to the cost of synthesizing novel structures . In order to fill the need for goal-directed benchmarking, Guacamol has been developed, which contains benchmarks such as rediscovery of and similarity to known active compounds. Although this benchmark is very practical for generative model utilized in lead optimization stage, the dataset is retrieved from ChEMBL and just removes the target compound from the training dataset, where then the task is to rediscover those removed compounds computationally. However, analogues may still remain in the training dataset (of which there are often many, given ChEMBL is constructed from publications which often contain SAR of related compounds), and suggested novel molecules may well be active although not being contained in the dataset, and hence also this type of validation has its shortcomings.