Dereplication is often described as the recognition of known natural products before resources are committed to repeated isolation and structure elucidation. In contemporary discovery, however, consequential decisions are increasingly made while chemical identity remains incomplete. LC–HRMS features, molecular formulas, MS/MS similarities, predicted compound classes, ranked structural candidates, mixture-level NMR evidence, and taxonomic occurrence each provide different forms of information, with different dependencies and failure modes. This article develops an original methodological decision architecture for using such evidence before full isolation without converting provisional inference into premature identification. The central proposal is to treat dereplication as an action-guiding process in which evidence is retained at its native analytical resolution, dependencies among evidence sources are made explicit, and contextual information modifies rather than substitutes for structural evidence. The architecture distinguishes analyte consolidation from annotation, relational similarity from identity, candidate generation from validation, and taxonomic plausibility from direct chemical observation. It further separates decisions about whether to deprioritize, re-interrogate, enrich, isolate, or escalate a feature from claims that its structure has been established. The framework is intended to improve transparency and resource allocation rather than provide a universal scoring rule. Its principal limitations are the incompleteness of spectral and occurrence databases, metric-dependent similarity, isomeric and stereochemical ambiguity, mixture complexity, correlated computational evidence, and variable taxonomic expression. Prospective testing against independently established structures will be required before claims of decision superiority are justified.
