In this regard, molecular similarity is one of the most used metrics to study the chemical complexity and diversity of a set of compounds within and outside the same class in a data set. At the same time, molecular similarity is used to create representative clusters based on molecular fingerprints, which allows us multiparameter and systematic criteria to establish molecular similarities between each pair of compounds. For example, the different versions of metal-based fingerprints proposed in this work, codify different kinds of descriptors like the presence of metal atoms in each compound, their oxidation state, and their atomic valences (Metal-FP1; 1621-bit), as other structural features like the presence of specific functional groups (Metal-FP2; 327-bit) and atom connectivity (Metal-FP3; 1185-bit). This strategy allows us to determine which combination of descriptors distinguishes different metal-based compounds. Namely, which combinations of descriptors are useful to study the molecular similarity of metal-based compounds.