Pool-based active learning techniques have had success producing multi-class classifiers that achieve high accuracy with fewer labels com- pared to random labeling. However, in an industrial setting where we often have class-level business targets to achieve (e.g., 95% recall at 95% precision for each class), active learning techniques continue to acquire labels for classes that have already met their targets, thus consuming unnecessary manual annotations. We address this problem by proposing a framework called Target-Aware Active Learning that converts any active learning query strategy into its target-aware variant by leveraging the gap between each class’ current estimated accuracy and its corresponding business target. We show empirically that target-aware variants of state-of-the-art active learning techniques achieve business targets faster on 2 open-source image classification datasets and 2 proprietary product classification datasets.