We investigate the composability of soft-ruleslearned by relational neural architectures whenoperating over object-centric (slot-based) repre-sentations, under a variety of sparsity-inducingconstraints. We find that increasing sparsity, es-pecially on features, improves the performanceof some models and leads to simpler relations.Additionally, we observe that object-centric repre-sentations can be detrimental when not all objectsare fully captured; a failure mode to which simpleCNNs are less vulnerable. These findings high-light the trade-offs between interpretability andperformance, even for models designed to tacklerelational tasks.