Abstract In the past 20 years, several novel data-mining methods, inclining mass defect filtering, background subtraction, isotope pattern filtering, and metabolomics analysis, have been developed and applied for drug metabolite profiling and identification by high-resolution mass spectrometry (HRMS). In addition, recently introduced HRMS-based data-dependent acquisition and data-independent acquisition methods, such as mass defect- and background exclusion-dependent acquisitions, MSE and SWATH, have greatly improved the speed and quality of biotransformation experiments carried out using HRMS. This chapter describes HRMS-based data acquisition and data-mining techniques for drug metabolite profiling and characterization and in silico tools for metabolite prediction and identification. Applicability of the HRMS technology to conducting common biotransformation experiments in support of drug discovery and development is also discussed with respect to special purposes and requirements of these experiments and unique advantages of individual HRMS techniques. Multiple real-life examples are included to demonstrate the usefulness of HRMS techniques in performing metabolite profiling and characterization experiments for lead optimization, selection of toxicology species for evaluating metabolite safety, and regulatory submission. Furthermore, current applications of HRMS-based technology in profiling and characterization of traditional Chinese medicine (TCM) components in samples from ADME studies of TCM in animals and humans are included.