The binding affinities of low energy poses are predicted using a deep learning based scoring function. Finally, FEgrow outputs binding poses in a form suitable for input to free energy calculations, and we illustrate this process with a case study, using the SOMD software to retrospectively compute relative binding free energies of several inhibitors of the SARS-CoV-2 main protease. In this way, we hope to integrate medicinal chemistry expertise in the FEgrow design workflow, with state-of-the-art methods for pose prediction, scoring and free energy calculation. By building ligands from the constrained core of a known hit, we maximise the use of input from structural biology, and reduce reliance on docking algorithms. We aim for an open-source, customisable, fast and easy-to-use (accessed through Jupyter notebooks) workflow that can adapt to community needs and advances in molecular design. The FEgrow package is written in Python, and supports Jupyter visualisation at each stage using py3Dmol. Underneath, the main unit in the package is RMol which extends the RDKit class rdkit.Chem.rdchem.Mol 31 with additional functionalities, such as visualisation, molecule merging, conformer generation, as well as storage of energies and other metadata. A convenience class RList is provided with the same functions for operating on a set of molecules, which allows also for future parallelisation. A modular workflow allows for addition/removal of functionality, such as new scoring functions or optimisation algorithms.