Ranked batch-mode active learning.

•We introduce a new way of thinking about Batch-Mode Active Learning.•A method for ranking unlabeled sets based on informativeness is proposed.•Our results are superior (up to 25%) to pool-based batch-mode active learning.•Our method is a drop-in replacement for batch-mode methods without their limitations.•It is also superior to density-sensitive active learning methods.