Abstract To learn with limited labeled data, active learning tries to query more labels from an oracle, while transfer learning tries to utilize the labeled data from a related source domain. However, in many real cases, there is very few labeled data in both source and target domains, and the oracle is unavailable in the target domain. To solve this practical yet rarely studied problem, in this paper, we jointly perform transfer learning and active learning by querying the most valuable information from the source domain. The computation of importance weights for domain adaptation and the instance selection for active queries are integrated into one unified framework based on distribution matching, which is further solved with alternating optimization. The effectiveness of the proposed method is validated by experiments on 15 datasets for sentiment analysis and text categorization.