The field of metaproteomics, the study of the collective proteome of whole (microbial) ecosystems, has seen substantial growth over the past few years. First coined in 2004, over 35% of all papers on metaproteomics have been published after January 2020 (PubMed query on ‘metaproteom*’). Despite its rapid rise, this field still suffers from low identification rates compared to single species proteomics because of two main reasons: (1) the dense fragmentation spectra caused by co-fragmentation, and (2) the profound lack of sequence resolution in traditional search engines. In this postdoc, I will seek methodological solutions to tackle these two issues, and therefore increase the identification rate in metaproteomics. The first solution can be found in analyzing co-fragmented spectra from both DDA as well as DIA datasets. The second solution will be to further extend our state-of-the-art tool MS2Rescore, where I will combine multiple search engines, including the co-fragmentation search engines from the first solution, and will add a new predictor for ion mobility. These methodological advances will be shown on two, hypercomplex application domains: meta-immunopeptidomics and open modification metaproteomics searches.