We present an algorithm for efficient acquisition of fluorescence microscopy data sets, a problem not addressed until now in the literature. We do this as part of a larger system for protein classification based on their subcellular location patterns, and thus strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information that has been scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive multiresolution scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to increase either time of space resolution of our data set, all while minimally affecting the classification accuracy of the entire system.