Chemical space could be defined as "a chemical descriptor vector space set by the numerical vector X encoding properties or molecular structure (descriptors) aspects as elements of the descriptor vector X" which was generated by a specific mathematical protocol. This definition emphasizes the influence of the descriptors and algorithms used to represent the structural, chemical, or atomic features of a dataset. Namely, a unique chemical dataset could generate a large variety of different yet valid chemical spaces that differ in their descriptors or mathematical protocol used to construct it, which is known as a "chemical multiverse", which term was coined based on an analogy in physics of the multiverse. In this work, the chemical multiverse is applied to illustrate the chemical spaces of MetAP DB compounds using different sets of descriptors. Figure shows three visualizations of three chemical spaces generated using three different sets of fingerprints, where Metal-FP1 (161-bit), which encodes the presence of metal atoms, oxidation states, and valence for each compound, Metal-FP2 (327-bit) besides codifying the presence of specific functional groups, and Metal-FP3 (118-bit) which also codifies the atom connectivity for each compound in the dataset. Interestingly, the use of fingerprints with a higher number of bits (i.e., Metal-FP2 and Metal-FP3) exhibits a better chemical space distribution for the most representative categories in MetAP-DB (i.e., metallodrugs and imagenology). These results suggest that Metal-FP2 and Metal-FP3 could be used as a starting point to develop predictive algorithms based on machine learning oriented to discover and develop new metal-based compounds, which is one of the current challenges in the molecular design area.