The large variety of molecular descriptors is linked to the subjectivity of the "molecular similarity" that is dependent on the molecular representation . Namely, the similarity of a pair of compounds depends on the features used to compare them. In fact, a pair of compounds could be considered similar if we use structural descriptors, but this does not guarantee that both compounds have similar in vitro activity . For this reason, it is crucial to evaluate the similarity of the compounds and, in general, the SPR of data sets using different descriptors and similarity metrics. The combined analysis of alternative representations (aka data fusion) could reduce the information gap between the chemical structures vital in drug development and biological knowledge. However, one of the most important issues in data fusion is assigning adequate weights to each variable that is being combined (e.g., dimensions that define the compound's chemical space) because different mathematical approximations could be used to generate them . In fact, there is no unique and "best" manner to generate consensus chemical spaces. Namely, it is necessary to adapt the data fusion approach to consider each data set. This important point could lead to the feature selection for prospective studies, generating a good starting point for exploring large datasets.