Bioisostere replacement is a powerful and popular tool used to optimize the potency and selectivity of candidate molecules in drug discovery. Selecting the right bioisosteres to invest resources in for synthesis and subsequent optimization is key to an efficient drug discovery project. Here we demonstrate how 3D-quantitative structure-activity relationship (3D-QSAR) and relative binding free energy calculations can be combined into an active learning workflow to prioritize molecules from a pool of hundreds of bioisosteres. We demonstrate on a human aldose reductase test case that the use of this workflow can rapidly locate the strongest-binding bioisosteric replacements with a relatively modest computational cost.