Using QCforever combined the black box optimization algorithms for discovering and designing materials, we have already reported the several results. Combining a deep learning based de novo molecule generator (DNMG) with machine learning and QCforever, we have successfully demonstrated that molecules designed in silico for optical absorption/emission can be realized experimentally. In addition, the DNMG proposed to use an material that had never received attention as an electret material. The DNMG becomes a molecular identifier by setting the computed property by QCforever NMR spectrum. In addition to the collaboration with DNMG, QCforever is useful for screening database. We have also employed QCforever with bound-Less Objective-free eXploration (BLOX) for searching out-of-trend materials from the database. Here, we demonstrate the database screening as an example of the use of QCforever. Recent development of material informatics increases the importance of experimental and computational databases of molecules. Although PubChemQC provides the observable molecular prop-erties like absorption wavelength, computational databases basically provides total energies and properties based on one electron states. They are might be important features but not practical properties. QCforever might be useful to translate another database to computational one with practical properties.