, where M is the total number of molecules in the entire library, m is the subset of molecules under consideration (e.g., the compounds in a certain density regime), K i is the number of occurrences of building block i in M molecules and k i its occurrences in the subset of m molecules. A large Z-score indicates that a building block appears more frequently in that subset compared to the rest of the library (or a random sample). By applying the hypergeometric distribution analysis, we can thus identify the building blocks with the largest impact on the target property and the degree to which they correlate with desired density values. Furthermore, we identify the building blocks that are prominent in particular density regimes and assess Z-score trends in density-ordered candidate subsets across the entire density range. In addition, we compute the average density values of the candidates derived from each building block, and analyze this data for trends. We employ the ChemML package for all data mining and pattern recognition tasks.