Abstract Evaluating the quality of ranking functions is a core task in web search and other information retrieval domains. Because query distributions and item relevance change over time, ranking models often cannot be evaluated accurately on held-out training data. Instead, considerable effort is spent on manually labeling the relevance of query results for test queries in order to track ranking performance. We address the problem of estimating ranking performance as accurately as possible on a ?xed labeling budget. Estimates are based on a set of most informative test queries selected by an active sampling distribution. Query labeling costs depend on the number of result items and item-speci?c attributes such as document length. We derive cost-optimal sampling distributions for commonly used ranking performance measures. Experiments on web search engine data illustrate signi?cant reductions in labeling costs.