After their introduction in the 1990s, Murcko scaffolds became popular for analyzing and comparing chemical databases . For example, in their work Bemis and Murcko analyzed a set of around 5000 drugs and found that ca. 25% of these drugs were represented by the 42 most frequently occurring Murcko scaffolds and ca. 50% by the 32 most frequent generic Murcko scaffolds. Moreover, Lipkus et al. found that half of the CAS registry, containing more than 24 million organic compounds at the time, could be described by only 143 generic Murcko scaffolds. Recently, Buehler and Reymond conducted a scaffold analysis of GDB-13 and compared it to public databases of known compounds such as ZINC and PubChem . The authors concluded that while Murcko scaffolds in public databases mostly contained linker bonds and six-membered rings, the Murcko scaffolds in GDB-13 had diverse ring sizes and ring systems without linker bonds. The scaffold tree was used for visual representation purposes in the Scaffold Hunter software , and in the compound screening selection protocols to ensure coverage of available chemotypes in the process of the discovery of a potent RAF Inhibitor . RCMF was used to analyze and compare DELs, containing 10 8 -10 12 compounds, in terms of their diversity by taking into account the number of compounds and Murcko scaffolds per RCMF class. The authors also showed the projection of chemical libraries onto a 2D RCMF map as a way to identify sparsely and densely populated parts of chemical space in a DEL. However, none of the above methods is 'ideal' (if such a thing exists) in terms of being able to group chemical structures in a meaningful way; and different methods represent different tradeoffs. With the aim to avoid the large number of singletons and small classes observed with Murcko scaffolds, as well as the unfavorable scaling of many clustering methods, the main focus of the current study is the SCaffold Identification and Naming System (SCINS) . SCINS is a descriptor of the reduced generic scaffold (Figure ), which is a further abstraction of the generic Murcko scaffold obtained by disregarding ring size, some chain length information and the topological connectivity of the generic scaffold. Figure illustrates the different terms in the SCINS descriptor and how each of them is obtained for the example in Figure . SCINS was originally applied to small sets of structures with the aim to group together compounds of similar biological profiles (activity and safety pharmacology) . In addition to providing an opensource implementation for general use, we apply SCINS to the analysis and comparison of two large databases: ChEMBL v33 and the Enamine REAL (Readily Accessible) Diverse subset in SCINS, Murcko and generic Murcko scaffold space. We examine the distribution of populations of the different SCINS classes to gain insights into their diversity. Additionally, we describe the strengths and weaknesses of the SCINS approach to classifying chemical space by a case study on a compound set tested against the dopamine receptor D2 (DRD2) obtained from ChEMBL . Since SCINS has not been made available for public use before, we open-source our Python implementation which is dependent only on the RDKit toolkit .