Abstract: Domain adaptive retrieval (DAR) has obtained the high-precision and fast retrieval across different domains by reducing domain discrepancies. However, existing works still face two issues: a) incorrect pseudo labels in the target domain cause error accumulation during training, and b) most existing methods are limited to single-label works and ignore the requirement for finer-grained retrieval. To address these problems, we propose a method called Dual Semantic Guidance and Cluster Matching for Domain Adaptive Retrieval (DSG-CM) . First, samples with high confidence in the target domain are gradually selected to reduce the impact of incorrect pseudo - labels. Second, the distinctiveness of samples is enhanced by incorporating multi-label semantic information from features with the class label information of the samples.Lastly, samples are divided into micro-clusters and the inter-cluster relationships between different domains are explored to enhance the retrieval precision. This method can generate compact and efficient hash codes, leading to superior performance. Experiment results on three benchmark datasets demonstrate that our method outperforms others.