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SignificanceMatrix completion is a fundamental problem in machine learning that arises in various applications. We envision that our infinite width neural network framework for matrix completion will be easily deployable and produce strong baselines for a wide range of applications at limited computational costs. We demonstrate the flexibility of our framework through competitive results on virtual drug screening and image inpainting/reconstruction. Simplicity and speed are showcased by the fact that most results in this work require only a central processing unit and commodity hardware. Through its connection to semisupervised learning, our framework provides a principled approach for matrix completion that can be easily applied to problems well beyond those of image completion and virtual drug screening considered in this paper.