In recent years, black-box optimization using machine learning (ML) algorithms as a predictor has achieved significant results in chemistry and materials science. ML itself is not limited to these disciplines and can be applicable in many disciplines by changing the evaluating system (evaluator). Similarly, the evaluator in black-box optimization decides what kind of materials and molecules we desired. If we can install experiments like synthesizing materials and measuring their chemical or physical values as the evaluator, we can obtain the desired materials. Surely, several examples of black-box optimization with the experiments as the evaluators appear in inorganic materials because synthesizing inorganic materials is more efficient than the simulation depending on the target properties. However, organic synthesis is not the case.