Μetabolomics enables the comprehensive profiling of small molecules in medicine, plant science, and systems biology. Its true value depends not on the number of detected features but on the reliability of metabolite identification and pathway analysis. Despite well-established guidelines, annotation and definitive identification are often conflated in practice. Simple matches in mass databases are frequently reported as identities, without comparison to standards or chromatographic evidence. This overstatement of confidence compromises validity and risks propagating errors into databases, pathway analyses, and AI-driven workflows. Mass spectrometry (MS) alone is rarely sufficient for identification and orthogonal evidence is essential. Chromatographic retention time is an underused but powerful descriptor reflecting molecular properties. When combined with MS it can provide plausibility checks and form the basis of Level 1 identification. Regulatory frameworks already require such combined criteria in targeted analysis. Systematic use of retention order, retention indices, and prediction models can filter implausible candidates and strengthen identification.