Finding novel molecules with specific properties is one of the main problems that drug discovery faces. One of the most common approaches to this is to explore chemical space by enumerating large virtual libraries, hoping to find a novel region of space containing useful structures. However, the drug-like chemical space is intractably large and a rough estimate would be at least 10 23 molecules . There are two classical approaches to exploring chemical space. One is to use implicit models, which do not store all molecules in a region of the chemical space but instead represent molecules indirectly. Techniques such as chemical space navigation by mutations or creating reaction graphs have proven to be successful . The other more common way is to use explicit models. By searching public databases that contain molecules obtained from various sources, e.g. ChEMBL , new molecules of interest can be discovered. An alternative approach is the GDB project, a set of databases that exhaustively enumerate a part of the chemical space.