Approximate Nearest Neighbor search via Product Quantization has become a popular approach for large-scale unsupervised image retrieval. However, existing systems primarily focus on real-world domains and lack evaluation in animation and cartoon contexts, where hierarchical semantics structure plays a crucial role in creative workflows. In animation production, artists often require flexible retrieval tools that can suggest visually coherent yet stylistically diverse content based on a given frame. To address this gap, we propose Cross-Quantized Hyperbolic (CQH) retrieval approach, which can capture hierarchical semantic similarity within the context of hyperbolic geometry. Besides, we contribute a so-called Cartoon18K dataset, the first large-scale dataset of cartoon images with multi-label hierarchical annotations. Extensive experiments on Flickr25K, NUS-WIDE, and CIFAR-10 demonstrate that CQH outperforms state-of-the-art unsupervised baselines. This accomplishment underscores the efficacy of our approach and contributes to the advancement of cartoon retrieval amidst the rapid proliferation of creative multimedia content.