<jats:title>ABSTRACT</jats:title> <jats:p> Genome sequencing projects in the last decade revealed numerous cryptic biosynthetic pathways for unknown secondary metabolites in microbes, revitalizing drug discovery from microbial metabolites by approaches called genome mining. In this work, we developed a heterologous expression and functional screening approach for genome mining from genomic bacterial artificial chromosome (BAC) libraries in <jats:named-content content-type="genus-species">Streptomyces</jats:named-content> spp. We demonstrate mining from a strain of <jats:named-content content-type="genus-species">Streptomyces rochei</jats:named-content> , which is known to produce streptothricins and borrelidin, by expressing its BAC library in the surrogate host <jats:named-content content-type="genus-species">Streptomyces lividans</jats:named-content> SBT5, and screening for antimicrobial activity. In addition to the successful capture of the streptothricin and borrelidin biosynthetic gene clusters, we discovered two novel linear lipopeptides and their corresponding biosynthetic gene cluster, as well as a novel cryptic gene cluster for an unknown antibiotic from <jats:named-content content-type="genus-species">S. rochei</jats:named-content> . This high-throughput functional genome mining approach can be easily applied to other streptomycetes, and it is very suitable for the large-scale screening of genomic BAC libraries for bioactive natural products and the corresponding biosynthetic pathways. <jats:p> <jats:bold>IMPORTANCE</jats:bold> Microbial genomes encode numerous cryptic biosynthetic gene clusters for unknown small metabolites with potential biological activities. Several genome mining approaches have been developed to activate and bring these cryptic metabolites to biological tests for future drug discovery. Previous sequence-guided procedures relied on bioinformatic analysis to predict potentially interesting biosynthetic gene clusters. In this study, we describe an efficient approach based on heterologous expression and functional screening of a whole-genome library for the mining of bioactive metabolites from <jats:named-content content-type="genus-species">Streptomyces</jats:named-content> . The usefulness of this function-driven approach was demonstrated by the capture of four large biosynthetic gene clusters for metabolites of various chemical types, including streptothricins, borrelidin, two novel lipopeptides, and one unknown antibiotic from <jats:named-content content-type="genus-species">Streptomyces rochei</jats:named-content> Sal35. The transfer, expression, and screening of the library were all performed in a high-throughput way, so that this approach is scalable and adaptable to industrial automation for next-generation antibiotic discovery.