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Next-generation sequencing technologies have revolutionized the field of virology by enabling the reading of complete viral genomes, extensive metagenomic studies, and the identification of novel viral pathogens. Although metagenomic sequencing has the advantage of not requiring specific probes or primers, it faces significant challenges in analyzing data and identifying novel viruses. Traditional bioinformatics tools for sequence identification mainly depend on homology-based strategies, which may not allow the detection of a virus significantly different from known variants due to the extensive genetic diversity and rapid evolution of viruses. In this work, we performed metagenomic analysis of bat feces from different Russian cities and identified a wide range of viral pathogens. We then selected sequences with minimal homology to a known picornavirus and used "Switching Mechanism at the 5' end of RNA Template" technology to obtain a longer genome fragment, allowing for more reliable identification. This study emphasizes the importance of integrating advanced computational methods with experimental strategies for identifying unknown viruses to better understand the viral universe.