Symbolic systems excel at reusing and composing modular functional units when solving problems such as simple analogical reasoning. However, they are less amenable to processing real-world data (e.g. images), and rely on additional (often hard-coded) mechanisms to convert such high-dimensional data to symbolic descriptions. In this work, we describe a modular approach ‘Neural Analogical Reasoning’ wherein elementary neural transformations operate and compose on distributed representations of high-dimensional inputs. We apply this approach on a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input-output images are related, so as to analogously transform future inputs. This can be viewed as a program synthesis task and solved via symbolic search if represented in symbolic form. Instead, we search for a sequence of elementary neural network transformations that manipulate distributed representations of the inputs. We present two variations of learning useful representations for this task and compare both with end-to-end meta-learning based approaches to demonstrate the importance of performing an explicit search.