However, considerable bottlenecks remain in metaproteomics data analysis, especially for data obtained from complex or poorly characterized environments. This is because metaproteomics studies traditionally identify peptides using search engines that rely on preexisting protein databases. While this approach works generally well in single-species proteomic studies, where annotated genomes can be used to create comprehensive protein sequence databases, it struggles with the large diversity and unknown nature of many microbial communities, which leads to incomplete, yet often overly large, databases. Recent advances in the accuracy and scalability of deep learning-based methods, however, have now made de novo sequencing a compelling alternative for metaproteomics, as de novo sequencing derives peptide sequences directly from the observed mass spectra, thus obviating the need for pre-existing sequence databases. This Viewpoint explores how de novo sequencing can address the inherent limitations of database-dependent methods in metaproteomics. We discuss its evolution, highlight its advantages, and provide a future perspective on how recent technological progress is expected to advance the field.