Large‐scale identification of metabolites is key to understanding metabolism at the systems level. Advances in metabolomics technologies, particularly ultra‐high resolution mass spectrometry enable rapid, comprehensive analysis of metabolites that is impractical to achieve by conventional methods. A significant barrier to meaningful data interpretation is the identification of metabolites including unknowns and the determination of their role(s) in metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm that efficiently detects functional groups within existing metabolite databases such as KEGG Ligand and the Human Metabolome Database, allowing for combined molecular formula and functional group queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both HMDB and KEGG demonstrated a high percentage of isomeric molecular formulae (43% and 28% respectively), indicating the necessity for techniques such as CS‐tagging. Furthermore, these databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism. In silico analysis of CS‐tagging strategies demonstrate that combined FT‐MS derived molecular formulae and CS‐tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database compared with 41% and 17% respectively without adduct formation.