Research on microbial metabolites is currently having a large impetus, given the growing body of knowledge about their relevance in multiple biological processes, including immunity, metabolism, nutrition, and the nervous system, and therefore in human health. The human body hosts trillions of microbial cells, mainly concentrated in the gut, and this microbial community exert their influence in host biological processes through the establishment of a myriad of molecular interactions between metabolites and protein receptors. This, in addition to the fact that the gut microbiome exceeds by three orders of magnitude the human genome (> 22 million vs 21000 human genes), has suggested the use of "metabolite-mimetic" molecules as drugs and nutraceutics that could modulate these interactions in pathological processes. As a matter of fact, this paradigm has spurred multiple drug discovery programs in the pharma industry that have reached clinical phases, and that include diseases such as inflammatory bowel disease (IBD), Crohn's disease, autism, Parkinson disease, multiple sclerosis, thrombosis, and atherosclerosis. In order to decode this interactome, multiple experimental approaches are being used and developed, including chemoproteomics, reverse screening of panels of proteins, and combinations of bioinformatic analyses and synthetic biology with screening. However, in this area the use of virtual screening tools could speed up the identification of interactions, as the predicted interactions could provide a set of hypothesis for fast, focused experimental tests, instead of blind and expensive all vs all screening. In this regard, we have recently provided a large set of virtual screening predictions to speed up the full characterization of the chemobiological space of food compounds. These interactions were retrospectively validated, and right now many of these interactions are being confirmed experimentally. In the present work we retrieve and analyze the full published set of interactions of gut metabolites with proteins, both human and gut microbial. A total of 2193 interactions are found, comprising 405 and 128 metabolites interacting with 451 human-and 56 bacterial targets, respectively. Patterns in this chemobiological space are identified, in terms of structures and chemical class of the metabolites, and of target class and biological origin of the proteins. After applying ligand-based virtual screening techniques, this set is expanded up to 9711 interactions (> 4-fold increase), now comprising 422 and 330 compounds, interacting with 1047 human-and 180 microbial ones, respectively. The interactions are analyzed again, and retrospectively validated experimentally, showing confirmation for predictions even for rather extrapolated chemical spaces. Finally, bioinformatic tools are used to identify subsets of "high priority" proteins and interactions of high biological relevance, both host and microbial. We expect that by providing the results of this work in an open, collaborative fashion, the clarification of the full chemobiological space of gut microbial metabolites will be significantly accelerated. This work will complement another recent one where we have analyzed and developed predictive models for the same set of molecules from the point of view of their structures, physicochemical properties, and distribution.