Tools available for analyzing next-generation sequencing (NGS) data produced from DNA-encoded library (DEL) screening campaigns are often constrained to customized methods developed internally by individual institutes, which usually generate data specifically focusing on protein-ligand interactions and based on distinguished criteria of compound recommendation. Existing approaches do not consider sequencing depth, sequencing error, and quality control when identifying candidate compounds. The analysis processes and criteria of compound recommendation for off-DNA synthesis and confirmation are highly time-consuming and subjective, significantly hindering the application of DEL screening in novel drug discovery. Here, to address these challenges, we developed an integral, accurate, and automated analysis workflow containing the tractability of the building blocks and DNA tags in split-and-pool cycles, 2D and 3D plots, and an enriched compound list, which was constructed based on computational analysis, artificial intelligence, and the experiential knowledge of medicinal chemists. This automated and standardized workflow was further validated through a showcase screening campaign on a novel antitumor target of CDK9. Novel hit compounds with high potency and selectivity were identified efficiently with minimal synthesis effort. The source code is available at https://github.com/kelly1210/PB-DELASA.