Glycosylation is one of the most important, but also most complex, post-translational modifications of proteins. Mass spectrometry-based glycoproteomics analysis offers a powerful approach to explore the fundamental roles of glycosylation, but achieving sufficient fragmentation for glycopeptide assignment remains challenging, especially for low-abundant glycopeptides and large, complex glycans. We introduce match-between-glycans (MBG), a lightweight, modular MS1-based method integrated into the FragPipe computational platform that expands glycopeptide identification by leveraging learned retention time (RT) and ion mobility (IM) shifts between glycan compositions. MBG can also identify glycans not included in the glycan database, such as those containing adducts or modifications, without expanding the search space. This submission includes original human plasma PASEF data (6 technical replicates, timsTOF HT) and re-analysis results from four publicly available datasets: fission yeast (PXD005565), mouse liver (PXD005553), mouse brain (PXD005411), and CPTAC glioblastoma (GBM, from the Proteomic Data Commons at https://pdc.cancer.gov/). MBG expanded glycopeptide identifications across all datasets, including 14.6% more in human plasma, and enabled detection of glycopeptide adducts (NH4+, Fe3+, Na+) and mannose-6-phosphate glycans.