In recent years, it has become increasingly common to use assays that can test whether two proteins interact, such as yeast two-hybrid and tandem affinity purification followed by mass spectrometry. Such techniques, particularly when applied at a large scale, suffer from high rates of false positives and false negatives. In addition, interactomic data sets are subjected to a number of biases, which limits considerably their usefulness to address biological questions. Interactomic databases only keep track of the positive results of protein interaction assays (those indicating that the tested proteins interact). Despite their importance, negative results (those indicating that the tested proteins do not interact) are not recorded in interactomic databases. Indeed, current interactomic databases do not support negative results. Here, I argue that systematically recording not only positive but also negative results of protein interaction assays would help scientists identify errors and deal with biases, thus enormously increasing the value of interactomic data sets. The challenges of implementing this change, along with potential solutions, are discussed.