Multi-scale feature extraction has played a crucial role in advancing RGB-based salient object detection. Many studies have achieved significant progress by designing sophisticated modules to strengthen multi-scale representations. Nevertheless, existing approaches typically employ fixed receptive fields, which is an inappropriate way to capture objects at different scales. Additionally, achieving balanced integration of low-level and high-level features during fusion remains a persistent challenge in developing efficient SOD architectures. To address these problems, we proposed a hierarchical scalable receptive fields search framework consisting of a novel searchable cell and a scene-aware search space. The proposed cell is able to ease the inconsistency between the sizes of receptive field and multi-scale features with an affordable budget. In addition, the search space explores the relationship between the contribution and the number of channels of multi-level features, exploring the appropriate number of the proposed cell for an efficient SOD model. Comprehensive evaluations on five benchmark datasets confirm the proposed method’s effectiveness, showing competitive performance against state-of-the-art approaches under four evaluation metrics. The source code will be publicly available at https://github.com/LiuTingWed/HSRF-SOD .