Computational methods play an integral role in the design-make-test-analyze (DMTA) cycle that drives realworld drug design projects . To address questions raised during this cycle, a single method does not suffice to deliver an answer; instead, a pipeline combining different methods can produce complementary and useful insights. Setting up such complex pipelines, however, can be difficult and time-consuming for many reasons: the scientist may not have had the training necessary to tackle these tasks , tools and their usage are constantly evolving (or deprecating), and feeding the output from one tool into another is often not straightforward. On top of these considerations, sustainable pipelines need to be findable, accessible, interoperable, and reusable (FAIR principles ) -not only today but in many years from now -to drive reproducible research.