Interactions between phototrophic algae and heterotrophic bacteria represent fundamental components of ecosystems and an increasingly harnessed tool in industrial processes, which however, remain difficult to characterise without complex community-metabolic modelling or targeted omics investigations. Using the marine alga Nannochloropsis oculata phycosphere as a model, a high-throughput screening framework was established to detect algal–bacterial interactions through a redefined growth-data scoring model based on laboratory measurements from 1,159 unique algae-bacteria consortia. Unlike conventional yield-based metrics that treat growth as an accumulated outcome, the score interprets N. oculata growth as a dynamic relief of system-level constraints, such as medium toxicity or the alga’s inaccessibility to essential substrates, arising from bacterial activities that modify the surrounding ecology. The score also defines an epistemic system for comparing consortia, in which information-theoretic surprisal was used as a quantitative measure of interaction strength derived directly from growth data, thereby reducing the need for prior omics input. Interestingly, the scores of screened consortia followed normal distributions, suggesting that neutrality dominates microbial community behaviour, upon which a “null theory” is proposed wherein true synergistic interactions are rare. This finding further highlights strong facilitation in specific partners including Flagellimonas sp., Devosia sp., and Mesorhizobium sp., which represent promising targets for detailed omics analysis. Additionally, a developing microdroplet-based extension of this framework will test natural consortia at the million-combination scale, enabling ecological-hypothesis verification and model training through systematic disassembly of complex communities, thereby facilitating the resolution of hidden microbial interactions and new functional discovery with potential for novel biotechnological applications.