I will give a brief introduction to sequential Bayesian experimental design, in the sense of greedy maximization of information gain. I will motivate the challenges this program places on approximate Bayesian inference, if it is to be used for high-dimensional signal acquisition optimization. I will outline a framework for variational Bayesian inference in large sparse linear models, with which BED can be implemented for such scenarios.