Gravity model of trade has emerged as an important and popular model in explaining and predicting bilateral trade flows. While the theoretical justification is no longer in doubt, nonetheless, its empirical application has however generated several unresolved controversies in the literature. These specifically concerns estimation challenges which revolve around the validity of the log linear transformation of the gravity equation in the presence of heteroscedasticity and zero trade observations. These two issues generate several challenges concerning the appropriate choice of the estimation techniques. This paper evaluates the performance of alternative estimation techniques in the presence of zero trade observations, checks for the validation of their assumptions and their behaviour in cases of departure from their assumptions, particularly the departure from the heteroscedasticity assumption. Analysis was based on a dataset of Africa's fish exports to the European Union, which contains about 70% zero observations. Given our dataset and the gravity equation specified, our results show that there is no one general best performing model, although most of the linear estimators outperform the GLM estimators in many of the robust checks performed. In essence, we find that choosing the best model depends on the dataset, and a lot of robust tests and advocate an encompassing toolkit approach of the methods so as to establish robustness. We concur with Head and Mayer (2013) that the gravity equation is indeed just a toolkit and cookbook in the estimation of bilateral trade flows.