In this study, we consider the cartography of inorganic crystal chemical space. Specifically, we address the combination of 2-4 elements to form stoichiometric inorganic compounds. This builds upon our earlier work by featurising each chemical composition using embedding vectors from machine learning models and labelling the entries to probe the distribution of known and unknown materials. The resulting hyperspace is reduced to two dimensions to produce visual representations that show hints of the innate separation between allowed and forbidden compounds.