Recent advances in structural biology, from sample preparation, to synchrotron infrastructure and data analysis pipelines, have transformed the throughput of protein-ligand complexes available to inform drug discovery campaigns. When soaked with carefully designed compound libraries, 2 the numbers of small molecule (or fragment) structural hits can reach 10s or 100s against a single therapeutic target. A frequently employed next step is to attempt to grow and/or link the hit compounds, using either custom synthesis 2 or ordering from catalogues of purchasable compounds. However, chemical space is vast such that even choosing follow-up compounds for purchase from on-demand libraries, such as the readily accessible (REAL) Enamine database 6 (> 5.5 bn compounds in 2022), becomes highly non-trivial. As such, attention is turning to cheminformatics and machine learning based algorithms for structure-based de novo hit expansion, linking and merging. A wide range of approaches are available to build from initial structural biology data, including DeepFrag 9 that identifies promising fragments for addition to an input bound ligand, using a deep convolutional neural network, and DEVELOP 10 that combines 3D pharmacophoric constraints from the binding pocket with a graph-based deep generative model for R-group and linker design.