RiPPs and NRPs can be chemically highly complex from various modifications and therefore often lack predictable structures, making them difficult to detect and identify using conventional proteomics workflows. As a result, they still need to be explored despite their crucial roles in AMR. A potential solution to identify RiPPs and NRPs is to screen for not-identified high-quality spectra (e.g., ranked by SPEQ ) and to leverage de novo sequencing and homologybased searches . These methods expand the search space beyond predefined peptide sequences and databases, allowing for the identification of non-tryptic peptides, homologous proteins, and NRPs. Moreover, integrating machine learning approaches and transfer learning can help refine peptide identification, making the detection of complex resistance mechanisms more accurate . Another approach for NRPs or RiPPs for which the BGC has been identified is either heterologous production of the BGC or manipulation of the producer organism itself, followed by comparison of the respective expression, deletion or overproducing mutants with the wildtype strain. Although greatly facilitating the identification of compounds derived from these BGCs, this approach requires genetic tools for the desired peptide producer and knowledge about the biosynthesis pathway.