While extremely promising, all of the above de novo design approaches suffer from some combination of the following issues: i) reliance on an approximate classical molecular mechanics force field or knowledge-based algorithm for generating and optimising binding poses, ii) use of an approximate objective function (usually a docking score) as a surrogate measure of binding affinity, iii) approximation of a rigid target receptor structure, and iv) limited synthetic tractability of the designed compounds. We therefore developed the FEgrow software as an open-source, interactive Jupyter notebook based workflow for building user-defined congeneric series of ligands in protein binding pockets to start to address some of these open questions (Figure ). FEgrow grows user-defined functional groups (R-groups) off a constrained core of a known hit compound, thus incorporating input from structural biology and the expertise of the user in selecting synthetically tractable elaborations. Since publication, we have added functionality for connecting R-groups to the core via a flexible linker, which can be chosen from a library of those common to bioactive molecules. In this way, users can choose from 1M+ combinations of linker and R-group from our distributed libraries (or upload their own R-group modifications). The modular workflow allows for the incorporation of state-of-the-art molecular modelling algorithms, such as the use of hybrid machine learning / molecular mechanics potential energy functions to optimise the ligand binding pose, and the gnina convolutional neural network scoring function to predict the binding affinity. We plan to expand the range of available optimisation algorithms and scoring functions as they become available (see Methods Section).