Data is a cornerstone for the generation of information and knowledge. Hence, data quantity and quality are vital to the development and performance of chemoinformatics and AI methods. Thus, academia, start-ups, and industry should, as a scientific community, prioritize access to data, which is as balanced and complete as possible. For example, data on the activities associated with ligand-target interactions should also include data on the inactivities associated with weak or non-existent ligand-target interactions. In that way researchers will be able to access the full spectrum of available knowledge . Moreover, such a "holistic" viewpoint would help cope with the data imbalance present in many drug design and compound optimization campaigns.