Analogies have been characterized as fundamental to abstraction, concept formation, and perception, and are traditionally expressed as quadruplets in the form of proportional analogies a : b :: c : d read "a is to b as c is to d". While Natural Language Processing (NLP) has primarily focused on word analogies and SAT problems, recent research has started exploring analogies between sentences and even documents. In this paper we explore the potential of identifying analogies between pairs of sentences via the identification of common latent relations between them. We exploit three different datasets generating pairs of sentences which can either share the same latent relation—forming thus an analogy—or not. We encode phrases into a higher dimensional vector space using embeddings from GloVe, BERT, and RoBERTa which we then feed to both a Multi Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). Results show that architectures using contextual embeddings as inputs outperform those based on static embeddings