Parallelly, the creation of domain-specific molecular representations used to combine data (of a specific domain) into new fingerprints, which contain chemical and non-chemical data, for example about biological activity, reactivity, side-effects, or any other properties. A key example of this is the evolutionary multipattern fingerprint (EvoMFP) , a novel molecular representation, generated by AI algorithms which combine substructures from SMARTS queries. Another example is the neural fingerprint which allows to combine chemical structural information with others, like bioactive or clinical information to create a unique fingerprint. The authors showed improvement in similarity-based virtual screening using this type of approaches inspired by the combination of different kinds of information. With the advent of large and ultra large chemical libraries has become the need to develop molecular representations that encode entire chemical libraries. One of such strategies is the database fingerprint and its natural extension, the statistical-based database fingerprint . Both approaches code in a single dimension the most significant bit present/absent in a compound database that can be of any size. The database fingerprints can be used with a variety of fingerprints, either general representations or domain-specific representations. However, for now there are limitations in the computational operations that allow us to generate molecular representations equivalent to a QR code in order to contain all the information available for a specific compound (e.g., drug-like properties, ADME, bioactivity, side effects, quantum descriptors, etc.). In the near future, the scientific community could develop more efficient methods for the analysis of twodimensional fingerprints (Chemical QR codes) that contain more information than what we can currently process with a traditional fingerprint. This will only happen if the combination of different disciplines and advances in hardware and software occur in parallel.