Natural products remain a major source of structurally diverse and biologically active small molecules, yet traditional activity-guided discovery is labor-intensive and prone to rediscovery, while untargeted genome mining often lacks efficient prioritization criteria for biosynthetic gene clusters (BGCs). Self-resistance-gene guided discovery has emerged as a powerful strategy to address this limitation. In producing organisms, toxic metabolites are typically accompanied by genetically encoded self-protection mechanisms, such as resistant target homologs, duplicated housekeeping genes, detoxification enzymes, repair systems, or transporters. When co-localized with BGCs, these determinants serve as functional markers for predicting bioactivity and, in some cases, molecular targets prior to compound isolation. Over the past decade, this concept has evolved into a target-directed genome mining framework supported by tools and databases including ARTS, FunARTS, antiSMASH, and MIBiG. This review summarizes the biological basis, workflow, representative advances, and limitations of this strategy. Self-resistance genes can thus be viewed as functional beacons for accelerating bioactive natural product discovery.