Person search is a challenging task that involves detecting persons and identifying their identities in images. Previous studies are conducted to address the need for large-scale datasets with bounding box and person identity labels via domain adaptation. Recent domain adaptive person search studies rely on softmax-based methods, facing overfitting issues in detection training to source domain. This paper proposes the use of energy-based out-of-distribution detection instead of softmax-based classifier. Our approach separates the distributions of person and background clutter without overfitting issues. The integration of energy-based techniques into the Domain Adaptive Person Search framework improves detection performance, with an average precision increase of 2.11% and 4.25% on CUHKSYSU and PRW datasets. These results highlight the potential of energy-based approaches for domain adaptive person search and pave the way for accurate person search applications in real-world scenarios.