Technologies such as batteries, biomaterials and heterogeneous catalysts have functionsthat are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare(1-5). Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches(6-14)but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water(15). The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm(16-18). This autonomous search identified photocatalyst mixturesthat were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous(19,20)free-roaming robot(21-24), automating the researcher ratherthan the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.