Several studies have investigated the effects of choices such as machine learning algorithm, sample selection protocol and total dataset size on active learning efficiency for experimental and computational affinity predictions. In general, active learning has been shown to increase enrichment of hits compared to either random or one-shot training of a machine learning model, at low additional cost, and to be relatively insensitive to choices of molecular representation, model hyperparameters and initial training subsets. Active learning has shown practical utility in prioritising compounds based on objective functions from docking or free energy calculations. Here, we interface FEgrow with active learning to efficiently search the chemical space of linkers and R-groups from a user-defined vector. As well as using a docking score to guide optimisation, we also experiment with functions that combine other molecular properties, such as molecular weight, and 3D structural information, such as protein-ligand interaction profiles (PLIP). To address the issue of synthetic tractability of the compound designs, we combine the workflow with regular searches of the Enamine REAL database to 'seed' the chemical search space with promising purchasable compounds. After testing and optimising the hyperparameters of the active learning models, we apply the algorithm to the prospective design of inhibitors of the main protease (MPro) of SARS-CoV-2, the virus responsible for the COVID-19 pandemic. This target has undergone extensive study in recent years. The COVID Moonshot Consortium used open science crowd-sourced designs, in combination with high-throughput structural biology and assays, free energy calculations, and machine learning driven synthetic route predictions, to generate a series of potent inhibitors. Other notable approaches that include biological confirmation of hits have employed, for example, structure-based design starting from a drug repurposing study, virtual screening of a curated collection of commercially available compounds, a deep reinforcement learning model using pharmacophore and substructure matches with known inhibitors, and a deep generative framework using only target sequence information as input (along with prioritisation based on factors such as docking score and retrosynthetic feasibility). Here we employ active learning to prioritise compounds for purchase and testing from the Enamine REAL database based only on early fragment hits. We suggest several novel designs that show activity in a fluorescence-based Mpro assay, as well as automatically generating several compounds that show high similarity to known Moonshot hits.