Most high-throughput experimental results of protein-protein interactions (PPIs) are seemingly inconsistent with each other. In this article, we re-evaluated these contradictions within the context of the underlying domain-domain interactions (DDIs) for two Escherichia coli and four Saccharomyces cerevisiae PPI datasets derived from high-throughput (yeast two-hybrid and tandem affinity purification) experimental platforms. For shared DDIs across pairs of compared datasets, we observed a remarkably high pair-wise correlation (Pearson correlation coefficient between 0.80 and 0.84) between datasets of the same organism derived from the same experimental platform. To a lesser degree, this concordance also held true for more general inter-platform and intra-species comparisons (Pearson correlation coefficient between 0.52 and 0.89). Thus, although varying experimental conditions can influence the ability of individual proteins to interact and, therefore, create apparent differences among PPIs, the physical nature of the underlying interactions, captured by DDIs, is the same and can be used to model and predict PPIs.