Natural products play a crucial role as sources of therapeutic agents for human being and agricultural pesticides. With the development of sequencing technologies, genome mining employing various bioinformatic tools has become an important approach for discovering more natural products. Due to the large number of natural product biosynthetic gene clusters, screening those capable of generating the most potent bioactive molecules has gained significance. To avoid self-destruction, some bioactive molecule producers have evolved with self-resistance enzymes, which are slightly mutated versions of original enzymes, but not sensitive to the bioactive compounds. The presence of self-resistance enzymes in the biosynthetic gene cluster of natural products serves as an indicator for the biosynthesis of bioactive compounds. On the other hand, the biosynthetic gene clusters of natural products could be located using information with their structures and activities as probes, e.g. the accumulating knowledge on antibiotic resistance mechanisms has facilitated the discovery of new antibiotics. Moreover, dereplication of natural products with known resistance mechanisms has been achieved by using indicator strains expressing the resistance genes. While these approaches have successfully utilized self-resistance genes to connect molecules with their biological activities, a more impactful application is to accurately link biological activity with genomic information through target-guided mining of natural products. The concept is to use a self-resistance gene as a predictive tool to screen and identify biosynthetic gene clusters encoding compounds that inhibit specific targets. Recent breakthroughs in self-resistance gene identification have bridged the gap between activity-guided and genome-driven approaches for natural product discovery and functional assignment. This review summarizes progress in bioactive natural product discovery guided by self-resistance genes, as well as its applications, which include the following points: 1) locating biosynthetic gene clusters based on self-resistance genes, 2) predicting the targets of secondary metabolites through self-resistance genes, 3) rapid dereplication of bioactive compounds with self-resistance mechanisms, 4) genome mining of bioactive natural products guided by the target and the internal connection with self-resistance genes, and 5) the development of genome data mining tools directed by self-resistance genes.