The IEEE international eScience conference, which emphasizes compute- and data-intensive research methods, bestowed the 2022 Best Paper Award on a multidisciplinary team that includes LLNL staff and external collaborators. The paper, “Scalable Composition and Analysis Techniques for Massive Scientific Workflows,” details the optimization of a drug screening workflow for the American Heart Association (AHA). The AHA Molecule Screening (MoleS) workflow combines specialized software tools to manage HPC hardware heterogeneity. The MoleS end-to-end workflow relies on both general-purpose and domain-specific software tools, some of which are open source and/or developed at LLNL: Maestro for workflow execution, Flux for workload management and scheduling, RabbitMQ for message brokering (orchestrated by Kubernetes), ConveyorLC for docking automation tasks, Fusion machine learning algorithms for binding affinity predictions, and GMD (Generative Molecular Design) for the small-molecule discovery loop. Read more on the LLNL Computing website.