In scientific articles, figures often convey experiments and observations, as well as physical intuition, to the readers. Yet, surface metadata alone can be insufficient to unpack how two scientific objects relate to each other. We explore this with AstroCLIMB, which infers relationships between pairs of figures and metafigures based on four classes: same figure, same paper, related papers and unrelated papers. We design a multimodal pairwise pipeline that consolidates the textual visual semantic, and domain-specific signal sources from object pairs. TF-IDF and SBERT text and image-based text-image feature pairings, combined with lexical overlap, entity, and instrument overlap, numerical overlap, and interaction features conditioned on methods, comprise the feature set fed into a weighted ensemble of LightGBM, XGBoost, and CatBoost trained with 313 engineered features on five-fold stratified cross-validation, optimizing for class-specific thresholds to produce a Macro-F1 of 0.5016 on out-of-fold data. Results show that yet stronger textual and multimodal counterpart representations exhibit complementary yet useful pairwise signals, although establishing related papers still remains a significant challenge.