This paper studies the problem of unsupervised domain adaptive hashing, which is less-explored but emerging for efficient image retrieval, particularly for cross-domain retrieval. This problem is typically tackled by learning hashing networks with pseudo-labeling and domain alignment techniques. Nevertheless, these approaches usually suffer from overconfident and biased pseudo-labels and inefficient domain alignment without sufficiently exploring semantics, thus failing to achieve satisfactory retrieval performance. To tackle this issue, we present PEACE, a principled framework which holistically explores semantic information in both source and target data and extensively incorporates it for effective domain alignment. For comprehensive semantic learning, PEACE leverages label embeddings to guide the optimization of hash codes for source data. More importantly, to mitigate the effects of noisy pseudo-labels, we propose a novel method to holistically measure the uncertainty of pseudo-labels for unlabeled target data and progressively minimize them through alternative optimization under the guidance of the domain discrepancy. Additionally, PEACE effectively removes domain discrepancy in the Hamming space from two views. In particular, it not only introduces composite adversarial learning to implicitly explore semantic information embedded in hash codes, but also aligns cluster semantic centroids across domains to explicitly exploit label information. Experimental results on several popular domain adaptive retrieval benchmarks demonstrate the superiority of our proposed PEACE compared with various state-of-the-art methods on both single-domain and cross-domain retrieval tasks. Our source codes are available at https://github.com/WillDreamer/PEACE.