Covalent modification of cellular proteins by chemically reactive compounds/metabolites has the potential to disrupt biological function and elicit serious adverse drug reactions. Information on the nature and binding patterns of protein targets are critical toward understanding the mechanism of drug induced toxicity. Protein covalent binding studies established in liver microsomes can quantitively estimate the extent of protein modification, but they provide little information on the nature of the modified proteins. In this article, we describe a label-free shotgun proteomic workflow for the identification of target proteins modified in situ by reactive metabolites in human liver microsome incubations. First, we developed a shotgun proteomic workflow for the characterization of the human liver microsomal subproteome, which consists of predominately membrane-bound proteins. Human liver microsomes were solubilized with a combination of MS-compatible organic solvents followed by protein reduction, alkylation, and tryptic digestion. The unmodified samples were analyzed by UHPLC-MS/MS, and the proteins were identified by database searching. This workflow led to the successful identification of 329 human liver microsomal subproteome proteins with 1% FDR (false discovery rate). The same method was then applied to identify the modifications of human liver microsomal proteins by a known reactive metabolite 2-(methylsulfonyl)benzo[d]thiazole (2), either after incubation directly with 2 or with its parent compound 2-(methylthio)benzo[d]thiazole (1). A total of 19 modified constituent peptides which could be mapped to 18 proteins were identified in human liver microsomes incubated directly with 2. Among these, 5 modified constituent peptides which could be mapped to 4 proteins were identified in incubation with 1, which is known to generate 2 in human liver microsomal incubations. This label-free workflow is generally applicable to the identification and characterization of proteins adducted with reactive metabolites in complex matrices and may serve as a valuable tool to understand the link between protein targets and clinically relevant toxicities.