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Liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics is constantly challenged by large-scale and unambiguous metabolite annotation in complex biological matrices, due to the enormous chemical and compositional diversity of metabolome. While standard tandem mass spectral databases have readily supported metabolite identification, the number of identified metabolites in untargeted metabolomics has remained limited. Over the past years, several phenomenal informatic and analytical approaches have been developed to strengthen metabolite annotation by improving coverage, accuracy, and unknown elucidation. Here, we review the major advancements of metabolite annotation strategies in LC–MS-based untargeted metabolomics, which include tandem mass spectral match and scoring algorithms, in-silico MS/MS spectral prediction, and the network-based approaches …