A method for managing agile sensors to optimize detection and classification based on discrimination gain is presented. Expected discrimination gain is used to determine threshold settings and search order for a collection of discrete detection cells. This is applied in a low signal-to-noise environment where target-containing cells must be sampled many times before a target can be detected or classified with high confidence. Bayes rule is used to compute the expected discrimination gain for each sample region using estimated probability that it contains a target. This gain is used to select the optimal cell for the next sample. The effectiveness of this approach was assessed in a simple test case by comparing the result of discrimination optimized search with direct search. For a single 0 dB Gaussian target, the error rate for discrimination optimized search was similar to the direct search result against a 6 dB target.