With increasing Internet user rates across Africa, there is considerable interest in exploring new, online data sources. Particularly, search engine metadata, i.e. data representing the contemporaneous online-interest in a specific topic, has gained considerable interest, due to its potential to extract a near real-time online signal about the current interest of a society. The objective of this study is to analyze whether search engine metadata in the form of Google Search Query (GSQ) data can be used to improve now-casts of maize prices in nine African countries, these are Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania and Uganda, Zambia and Zimbabwe. We formulate as benchmark an auto-regressive model for each country, which we subsequently augment by two specifications based on contemporary GSQ data. We test the models in in-sample, and in a pseudo out-of-sample, one-step-ahead now-casting environment and compare their forecasting errors. The GSQ specifications improve the now-casting fit in 8 out 9 countries and reduce the now-casting error between 3% and 23%. The largest improvement of maize price now-casts is achieved for Malawi, Kenya, Zambia and Tanzania, with improvements larger than 14%.