The existing multi-label hash retrieval methods fail to adequately capture the fine-grained distinctions between image pairs during similarity evaluations. To address this limitation, we propose a novel fine-grained similarity evaluation method for multi-label image pairs, upon which we develop a fine-grained similarity-based multi-label remote sensing image hash retrieval (FMHR) framework. Specifically, the developed evaluation method establishes hierarchical criteria that systematically account for both common and distinct labels between image pairs. FMHR leverages the proposed evaluation framework to extract multi-dimensional discriminative features from remote sensing images. The experimental results on three public multi-label remote sensing datasets demonstrate that the FMHR approach outperforms other methods in terms of retrieval quality and ranking accuracy.