Effective data discovery is crucial for collaboration and innovation in data sharing contexts. Data spaces combine the secure sharing features of data ecosystems with economic aspects of data markets in a federated environment. Traditional data catalogs, which primarily rely on high-level metadata (e.g., dataset name, license, keywords), often fail to adequately convey dataset utility to potential consumers. Our solution proposes content-based catalogs to enhance data discovery within data spaces through three key components: (i) high-quality descriptive metadata, (ii) representative data samples, and (iii) advanced discovery services. These components enable consumers to effectively find datasets that align with their requirements and evaluate their relevance prior to access. In this paper, we demonstrate through extensive experimentation across multiple contexts and data quality levels that our sampling technique significantly enhance dataset discoverability while preserving data provider sovereignty.