Correct annotation of function is essential if one is to take full advantage of the vast amounts of genomic sequence data. The accuracy of sequence-based functional annotations is often variable, particularly if the sequence homology to a known function is low. Indeed recent work has shown that even proteins with very high sequence identity can have different folds and functions, and therefore caution is needed in assigning functions by sequence homology in the absence of experimental validation. Experimental methods are therefore needed to efficiently evaluate annotations in a way that complements current high throughput technologies. Here, we describe the use of nuclear magnetic resonance (NMR)-based ligand screening as a tool for testing functional assignments of putative enzymes that may be of variable reliability. The target genes for this study are putative enzymes from the methanogenic archaeon Methanosarcina acetivorans (MA) that have been selected after manual genome re-annotation and demonstrate detectable in vivo expression at the level of the transcriptome. The experimental approach begins with heterologous E. coli expression and purification of individual MA gene products. An NMR-based ligand screen of the purified protein then identifies possible substrates or products from a library of candidate compounds chosen from the putative pathway and other related pathways. These data are used to determine if the current sequence-based annotation is likely to be correct. For a number of case studies, additional experiments (such as in vivo genetic complementation) were performed to determine function so that the reliability of the NMR screen could be independently assessed. In all examples studied, the NMR screen was indicative of whether the functional annotation was correct. Thus, the case studies described demonstrate that NMR-based ligand screening is an effective and rapid tool for confirming or negating the annotated gene function of putative enzymes. In particular, no protein-specific assay needs to be developed, which makes the approach broadly applicable for validating putative functions using an automated pipeline strategy.