Autonomous atomistic computations are excellent tools to accelerate the development of heterogeneous (electro-)catalysts. In this perspective, we critically review the achieved progress to accelerate high-throughput screening aimed at identifying promising catalyst materials via databases, workflow managers, and machine-learning techniques. Outstanding challenges are also discussed extensively: the modification and stability of catalyst surfaces under realistic reaction conditions is key for meaningful predictions. Furthermore, adequately accounting for solvent effects remains a topic of active research particularly relevant for biomass transformations and electrocatalysis. Finally, efficient, autonomous workflows for investigating active sites of amorphous catalysts remain underdeveloped. The computations can also be supplemented with autonomous laboratories, which allow the performance of sophisticated experiments driven by artificial intelligence-augmented design of experiments, reducing human-time investment for optimizing synthesis and reaction conditions as well as catalyst characterizations. The combination of autonomous computations and laboratories promise to power the dearly needed transition to a sustainable chemical industry.