Abstract: In many intelligence agencies, the screening of data into usable information ready for analysis poses a significant bottleneck. Typically, much more data is available than what can be screened in the allotted time. We call the staff that screens raw data into usable information processors. We formulate the problem faced by an intelligence processor — selecting which data to screen — as an exploration-exploitation problem: the collector has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the collector’s knowledge and examine algorithms that allow the collector to maximize the discovery of relevant data given a time limit. We computationally test the model and gain insight into solutions using a simulated intelligence data set based on the Enron social network and email corpus. Operations Research & Industrial Engineering University of Texas at Austin