Metal-organic cages (MOCs) are modular supramolecular structures that are prepared from both organic and inorganic building blocks through self-assembly. Metal complexes typically form the vertices of these polyhedral architectures, while organic linkers either span the faces or the edges. Their internal cavities are capable of binding guests with high affinity and selectivity, enabling discrimination between chemically similar molecules and allowing potential applications in chemical purification, 1-3 sensing, and catalysis. Generally, new MOCs are designed based on concepts uncovered in previous work, where new structures are created by carrying out small alterations to the building blocks and tuning the assembly conditions. The structural diversity of reported MOCs has been achieved through changing the topicity, shape, or flexibility of the ligand, the metal ion or counterion identity, or the solvent, temperature, or concentration. For example, a small change in building block geometry can lead to a major impact on the resulting MOC architecture and characteristics. The exploratory and serendipitous nature of MOC discovery therefore often leads to wasted resources when no stable structures are observed under a range of reaction conditions. The application of both automated experimental and computational screening, however, has the potential to help accelerate this design and discovery process. Previously, our work has focused on the automated discovery of organic supramolecular assemblies, including porous organic cages and catenanes, which was further streamlined into a lowcost, open-source workflow which combined automated synthesis, characterization and analysis. This workflow was designed to be translatable to other supramolecular architectures. Computational investigations of MOCs to derive design rules and fundamental understanding are still relatively scarce, but notable contributions from Duarte et al. and Tarzia et al. demonstrate the value of using computational screening to better understand the MOC self-assembly process, enabling desired architectures to be targeted and experimental outcomes to be rationalized. Figure 1. Summary of the automated workflow for the assembly and property screening of metal-organic cages (MOCs): (Top left, red) High-throughput automated experimental synthesis, including the setup of an Opentrons OT-2 liquid handing platform for screening the assembly of a metal ion (M) with two triamines (A and B) and four aldehydes (1-4), yielding eight ligands (L = A1, A2, A3, A4, B1, B2, B3, B4), screened with three counterions (C); (Top middle, orange) Automated characterization and analysis of H NMR spectra and highresolution mass spectrometry to assess the assembly outcomes from the synthetic screen; (Top right, purple) Outputs of the automated data analysis were used to identify interesting 'hits' to take forward for property screening; (Middle, green) Conventional batch scale-up of any identified hits; (Bottom left, pink) Computational modelling for visualization and to help rationalize experimental outcomes; (Bottom right, blue) Automated host-guest assay of 32 guests to probe binding properties of the scaled up hits from the automated screen.