Comics combine visual art, text, and narrative pacing, making them highly engaging but difficult to search using metadata or conventional content-based retrieval. Existing comic search platforms often ignore what comic fans often seek, such as a visual style, character type, or fast narrative flow. LLM or AI-based search performs well on text and images but is often opaque, leaving users unsure why results are relevant. We introduce ESCOMIC, an adaptive and explainable search system for multimodal comic content. In a query-by-example setting, ESCOMIC combines low-level visual and textual features with high-level comic facets (e.g., genre, character gender, story pace) in a facet-based re-ranking framework. Implicit feedback from simple interactions (e.g., hovering) dynamically adapts facet weights to evolving intent. ESCOMIC provides global explanations that show how facets shape ranking and local explanations that compare top-k results to the query. Due to the lack of comic retrieval benchmarks, we conduct a controlled user study with eye-tracking and appropriate baselines. Results show higher retrieval effectiveness, satisfaction, and trust with adaptive, content-aware explanations. Our findings highlight the value of transparent multimodal search and motivate future work using LLM and RAG based narrative explanations.