An unfortunate reality is that modern science is often limited by the number of experiments that one can afford to perform. When faced with budget constraints, choosing the most informative set of experiments sometimes requires intuition and guess-work. Here, we describe a data-driven method for prioritizing experimentation given a fixed budget. This method involves first predicting the readout for each hypothetical experiment and, second, using submodular optimization to choose a minimally redundant set of hypothetical experiments based on these predictions. This approach has several strengths, including the ability to incorporate soft and hard constraints into the optimization, account for experiments that have already been performed, and weight each experiment based on anticipated usefulness or actual cost. Software for this system applied to the ENCODE Compendium can be found at https://github.com/jmschrei/kiwano.