GoShifter is suited to investigate whether a specific annotation might be effective in fine mapping known genetic loci for a trait. Consequently, GoShifter does tend to favor high-resolution annotations (e.g., annotations with a small average size). But in certain instances, large annotations (e.g., super enhancers or large gene sets) might be important indicators that separate trait-associated variation from non-trait-associated variation.56 Although informative, these annotations might not be particularly effective for fine mapping. Under these circumstances, SNP matching might be a necessity. We note with caution, however, that the importance of individual matching parameters might be different depending on which specific annotations are being tested. Importantly, matching on the number of SNPs in LD will be critical: the number of SNPs that a variant is in LD with will be proportional to the chance that one of those variants overlaps an annotation; consequently, failing to control for LD invariably yields inflated results. In certain instances, controlling for LD alone might substantially mitigate type I error, but in most instances, matching on LD alone will not be adequate. Additional parameters should be carefully considered and evaluated (e.g., by simulation experiments) when SNP-matching-based enrichment tests are used. For example, in the assessment of DHS enrichment, our results demonstrate that GEN, TSS proximity, and TES proximity are additional important parameters. We speculate that to accurately detect the enrichment in exons, it might be necessary to further match on other parameters, such as the number of exons and gene length.