In the last two decades, the use of online resources in educational settings has seen an unprecedented growth. Regrettably, students' online inquiry competences (OIC) are not necessarily well developed to face problems involving information intensive domains. While different OIC development approaches have been proposed to address this situation, these fail in timely identifying their effects on students' OIC applied to practical search scenarios. To address this drawback, in this article we study models to predict students' search performance in the context of an OIC evaluation test. Our approach focuses on exploiting demographic, behavioral, cognitive, and affective features, to predict - at four points of the overall search process - whether students succeed or fail in finding relevant documents to accomplish a research task. Our preliminary results show that it is possible to anticipate the overall search performance of students with moderate accuracy at the 25%, 50%, 75%, and 90% of the search session progress. These findings illustrate potential benefits and limitations of using non-obstrusive aggregated signals to timely predict search performance in learning contexts.