In medicinal chemistry and drug design, the description of properties, structures, and geometries is the base of good property predictions and is the cornerstone of rational molecular design. However, in the realm of inorganic medicinal chemistry, the high diversity of the atomic, structural, and tridimensional properties of metal-containing compounds challenges the generation of rational design models. Furthermore, a new trend for the development of metal-organic frameworks increases the complexity of condensing and decoding their data. In this regard, one of the most common approaches to coding properties, structure, and geometric data is the use of molecular fingerprints. Which could be defined as "a unidimensional representation of the properties or substructures present in molecules." Several molecular fingerprints have been developed which are discussed in depth in a recent review. Interestingly, most of the fingerprints have been developed to codify specific and predefined features (also called dictionary-based fingerprints; e.g., MACCS keys), topological and electronic descriptors (e.g., EState), or by an independent and mathematical description of the molecular characteristics (a representative example are circular fingerprints; e.g., ECFP4). Also, consensus or hybrid fingerprints have been developed to increase their description power, covering descriptors previously ignored (e.g., chirality). All of them have direct and proven applications around molecular design and virtual screening, assessment of molecular diversity, and structureproperty prediction algorithms, which are widely used in modern medicinal chemistry. However, most molecular fingerprints have been developed for small organic compounds with developments to extend their applications to macromolecules and more complex compounds (e.g., proteins, peptides, and natural products), which emphasize the need to develop novel fingerprints capable of encoding data from inorganic or metal-containing compounds. Open molecular databases have played a key role in the development of advanced molecular design. Especially, for complex molecules where the data is non-existent, limited, and/or unbalanced. Interestingly, for the metal-based compounds there are no molecular databases dedicated to studying these, although the relevance and the deed have been highlighted earlier. To address these needs, the goal of this manuscript is to introduce Metal-FP: a hybrid molecular fingerprint to encode the chemical structures of metal-based approved drugs. The hybrid fingerprint can be the basis of a general framework to represent metal-based bioactive compounds (investigational and under clinical development).