Scientific discovery, particularly in disciplines such as physics and biology, often asks if there remains room for truly transformative innovations. But microbiome research faces considerable challenges stemming from the intricacies of microbial communities. These are subject to dynamic processes, such as horizontal gene transfer, and exhibit great variability across different spatial scales. Genomic sequencing and molecular biology removed the constraint of traditional isolation techniques. Microbiome research typically unfolds along two distinct trajectories: exploration and hypothesizing. However, exploratory studies emphasize sequencing entire communities to develop foundational datasets, while hypothesis-driven research adopts a more structured framework aimed at testing specific ecological theories. Merging these approaches, particularly through the integration of emerging technologies such as machine learning, holds significant potential for driving future discoveries. The combination of these methodologies considered may help unravel critical ecosystem services rendered by microbial communities, both within host-associated systems and in broader environmental contexts.