Over the past few years we have worked on an approach to matching features between images we takes full advantage of the priors which are normally available to avoid blanket, bottom-up image processing and proceed in a sequential, guided manner. In Active Matching, each measurement of one feature is used to dynamically and probabilistically update predictions of the positions of the other candidate features. In this way, image processing can be put "into the loop" of the search for global consensus, producing matching algorithms which are much more satisfying than RANSAC or similar which depend on random sampling and fixed thresholds. The decisions which must be taken at each step are determined based on explicit evaluation of expected information gain. I will explain the basic Active Matching algorithm, and recent developments which now allow us to match hundreds of features per frame in real-time.